Top PDF Aerial Object Detection using Learnable Bounding Boxes

Aerial Object Detection using Learnable Bounding Boxes

Aerial Object Detection using Learnable Bounding Boxes

ix corresponding point. The sum of all these distances are taken for each case. The case that gives the minimum sum of distances will be selected as the optimum configuration. We use this process to select the best anchor box as well. In this figure, the case on the top left will give the least sum of distances.................................................................................................................................... 50 Fig 10.2: The activation function, sinh(x/3) used to predict the displacement of anchor points. ....................................................................................................................................................... 52 Fig 10.3: arcsinh(x), showing its good curve and gradient. .......................................................... 53 Fig 11.1: Detection visualization for YOLO v3, without any rotation. The pink box and circle are detections while blue circles are ground truth. .............................................................................. 61 Fig 11.2: Detection visualization for YOLO v3, with +/- 10-degree rotation. The pink boxes are detections while blue circles are ground truth. Note the mistaken identifications which contributed to low scores. ................................................................................................................................ 62 Fig 11.3: Detection visualization for YOLO v3, with +/- 25-degree rotation. The pink boxes are detections while blue circles are ground truth. The network detects objects that are rotated by almost +/- 25 degrees, giving false predictions. ............................................................................ 63 Fig 11.4: Detection visualization for YOLO v3, with +/- 45-degree rotation. The pink boxes are detections while blue circles are ground truth. The network detects large vehicles as small vehicles which leads to lower scores............................................................................................................ 64 Fig 11.5: Detection visualization for YOLO v3 Extra Anchors. The pink boxes are detections while blue circles are ground truth. The network accurately detects objects of all rotations. ....................................................................................................................................................... 66 Fig 11.6: Detection visualization for Deformable YOLO. The pink boxes are detections while blue circles are ground truth. The network predictions are not restricted to rectangles or squares as can be seen in the top right car images.................................................................................................. 67 Fig 11.7: Examples of cases where the model results were penalized based on arbitrary rotation……………………………................................................................................................... 71
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Object Detection using Deep Learning

Object Detection using Deep Learning

Autonomous vehicles, surveillance systems, face detection systems lead to the development of accurate object detection system [1]. These systems recognize, classify and localize every object in an image by drawing bounding boxes around the object [2]. These systems use existing classification models as backbone for Object Detection purpose. Object detection is the process of finding instances of real- world objects such as human faces, animals and vehicles etc., in pictures, images or in videos. An Object detection algorithm uses extracted features and learning techniques to recognize the objects in an image. In this paper, various Object Detection techniques have been studied and some of them are implemented. As a part of this paper, three algorithms for object detection in an image were implemented and their results were compared. The algorithms are “Object Detection using Deep Learning Framework by OpenCV”, “Object Detection using Tensorflow” and “Object Detection using Keras models”.
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Object Detection Using SURF and Superpixels

Object Detection Using SURF and Superpixels

In the area of intelligent systems, the autonomous mobile robots are expected to have the ability to recognize their surrounding environment in real time. Object detection, which is a task for searching and localizing a target in a particular scene, can be considered as prime feature for autonomy. This fact has stimulated the research in this field and as a result several algorithms have been pro- posed during the last several years. Lai et al. [1], Ozuysal, et al. [2], Harzallah, et al. [3], Dalal and Triggs [4] pro- posed to use the standard sliding window approach in which the system evaluates a score function for all posi- tions and scales in an image; and sets limits to the scores to obtain bounding boxes for each instance. Each detec- tor window has a fixed size and search across 20 scales on an image in a pyramidal form. For efficiency a linear score function is considered. The performance of the classifier heavily depends on the data and also the fea- tures used for the object detection [1]. Another popular approach is to extract local interest points from the image and then to classify each of the regions around these points, rather than looking at all possible sub windows
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Deep Nuisance Disentanglement for Robust Object Detection  from Unmanned Aerial Vehicles

Deep Nuisance Disentanglement for Robust Object Detection from Unmanned Aerial Vehicles

• Faster-RCNN: Faster-RCNN [15] is a two-stage detector which is a standard benchmark in the object detection community. It is a descendant of the RCNN family of object detectors which have traditionally used a region proposal algorithm (such as Selective Search) to com- puter probable object locations in an image. These probable object locations called regions of interest are then passed on to a CNN to extract features, localize objects and classify the objects in the image to different classes. Faster-RCNN came up with a novel Region Pro- posal Network(RPN) which is nothing but a small object detector in itself to propose regions of interest by classifying certain predefined boxes of varying sizes and aspect ratios into two classes: object or no object. This RPN can be trained by backpropagation similar to the main object detector using the classification and the localization losses. The ROI pooling layer converts various different-sized regions of interest detected by the RPN into a fixed size so that it can be further fed into the fully connected layers.
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OBJECT REORGANIZATION AND TRACKING USING UNMANNED AERIAL VEHICLE

OBJECT REORGANIZATION AND TRACKING USING UNMANNED AERIAL VEHICLE

The algorithm has been developed for real-time object detection and tracking using color feature and motion. Tracking of the object is done on the basis of region properties such as centroid, bounding box etc. Here, motion detection and tracking is done using background subtraction and optical flow method. Most of the time median filtering is used in image processing to remove noise during real-time object detection and tracking. Median filtering is far better the convolution technique when the aim to prevent edges and to eliminate noise. Specify the characteristics or property of video input. Begin with video acquisition. Separate the frames from video input. After separating frames from the acquired video generate image after subtraction contains motion region and noise. Median filter is used to eliminate noise. Morphological technique is used for further processing. Vertical along with horizontal projection is utilized to detect the height of motion part. Make the Video Device framework object. Make a framework object to calculate path and velocity of object movement from one video frame to another utilizing optical flow technique. Set up the vector field lines.
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Real Time Object Detection in Images using YOLO

Real Time Object Detection in Images using YOLO

ABSTRACT: OpenCV is a library of programming functions mainly aimed at real-time computer visionsuch as object detection. Object detection framed as a regression problem to spatially separated bounding boxes and associated class probabilities.YOLO, an approach to object detection, works on object detection repurposes classifiers to perform detection.. Single neural network predicts the bounding boxes and its class probabilities directly from full images. It optimized end-to-end directly on object detection performance. YOLO architecture is extremely fast, processes images in real-time at 45 fps.FastR-CNN use region proposal methods to first generate potential bounding boxes in an image and then run a classifier on these proposed boxes. Post-processing is used to refine the bounding boxes, eliminate duplicate detections, and rescore the boxes based on other objects in the scene.YOLO makes fewer background mistakes than Fast R-CNN. In this paper YOLO and fast R-CNN is combined to produce better results.
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Object Detection in High Resolution Aerial Images and Hyperspectral Remote Sensing Images

Object Detection in High Resolution Aerial Images and Hyperspectral Remote Sensing Images

image, the maximum width and height of the anchors are chosen as 0.25 of the spatial size of the input image. Accordingly, we set the aspect ratios of the anchors as 1, 2, 0.5, 3 and 0.33. These anchor bounding boxes are then compared with the ground truth locations with the intersection over union (IoU) score [137] to determine the presence of objects. If the anchor bounding box obtains the IoU score higher than a certain threshold, it is used for predicting the object locations and categories. To get bounding box locations and the corresponding object class, two convolution layers are attached to each output feature map. The first convolution layer aims at location estimation and predicts the offset from each anchor to its corresponding matched ground truth location. It consists of 4A filters of spatial size 3 × 3, where A is the number of predictions per location and 4 denotes the number of bounding box offset parameters, including horizontal and vertical locations of the bounding box centers and the height and width of the bounding boxes. The second convolution layer is responsible for estimating the probability of the anchor being classified as the desired objects. By employing the one-hot vector representations, the convolution layer contains (K + 1)A filters, where K is the number of object classes. During the training process, we aim at minimizing the objective loss function (L) that contains both localization (L loc ) and classification (L cls ) parts:
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Spatial object median rules in bounding-volume hierarchies for complex environments

Spatial object median rules in bounding-volume hierarchies for complex environments

Hierarchical representation has not been the main focus in term of using them for collision detection in complex environment. From thorough discussion of collision detection in complex environment above, the method that involving hierarchical representation is on grid system and height map system to detect collision (Cohen, Manocha et al. 1994; Tecchia and Chrysanthou 2000; Laycock, Ryder et al. 2007). By using so called grid system, the collision is to be determined once the object or crowd has intersected with any grid boxes. However, this method is far from more specific approach of hierarchical representation which is Space subdivision and bounding-volume hierarchies (BVH). BVH usage is more on general collision detection in virtual environment and from the study, BVH has only very little literature in large-scaled simulation such as massive environment simulation and complex environment.
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Moving Object Detection and Segmentation for Remote Aerial Video Surveillance

Moving Object Detection and Segmentation for Remote Aerial Video Surveillance

shadows are visualized in Fig. 1.2 (c). As the shadows of moving objects are moving, too, it is probable that they are detected and misleadingly treated as part of the objects or even as individual objects, also known as False Positive (FP) detections. This can be a problem especially when multiple vehicles are driving in a group one behind the other with shadows between them. The detection algorithm may interpret this group of objects moving in-line as a single object. The potential benefit of temporal information is shown in Fig. 1.2 (d). Two trucks are driving next to each other. At time step t , a tree next to the street is partially occluding the right truck. A missed detection, also known as False Negative (FN) detection, is likely to occur in this situation. There is no occlusion at time step t − 20 and both trucks are clearly visible. Learning this information can help to handle the occlusion situation in time step t. While five of the images (a, b, c, d, and f ) come from datasets collected by the Luna UAV, Fig. 1.2 (e) originates from the VIVID dataset [Col05]. In this sequence, six vehicles drive one behind the other on a runway. Significantly different altitude and camera view angles lead to large deviations in vehicle appearance. A vehicle detection algorithm is supposed to be general enough to compensate for this intra-class variability while still being specific enough to reject non-vehicles [Hal06]. Transferability is then given by applying the same method with good performance for both Luna and VIVID videos. Finally, in Fig. 1.2 (f ), a scene is shown with 17 vehicles driving on a busy urban street. Each vehicle is manually labeled with a red bounding box. Such kind of manual labeling is called Ground Truth (GT) and can be used to evaluate automatic detection approaches. In order to meet real-time requirements, all vehicles have to be detected and tracked in parallel with a processing time of less than 40 ms per image. Consequently, a multiple-step processing chain solving these tasks must therefore employ very efficient algorithms.
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Object Detection from Images Using Deep Learning

Object Detection from Images Using Deep Learning

Object detection is an active point form of research and development for over a decade. A major drawback of traditional computer vision approaches was that the features for constructing object classifiers and detectors were hand- designed. some of the new work in object detection and recognition revolves around the need to speed up detection phase of recognition pipeline A different closer work to ours, depends on thought that objects must be localized without having to find their category a particular of these methodology generate on bottom-up classless segmentation. The task of object localization and detection has been largely addressed by using CNNs and formulating the detection problem as that of classification of a smaller region, thus obtaining a reduced spatial map of classifications. One of the most advancement in this area was made by introducing R-CNN type of networks, in three iterations R-CNN [1], Fast R- CNN [2][7] and Faster R-CNN [3]. We can say ultra-modern techniques for classifying and detecting items of general categories are mainly dependent on deep CNNs. Regions with Convolutional Neural Networks (R-CNN) presented Girshick et al. [3] in multi-stage for the classification of region proposals to find objects explains the detection issue into a few steps such as CNN pre-training, bounding-box proposal, SVM training, CNN fine-tuning, and bounding box regression. It accomplishes great results however the pipeline is less effect since features of each region proposal necessary be computed over and again. In SPP-net [9], this issue has been self-addressed by presenting a pooling method to compute the feature map just once and create features in arbitrary regions. Faster R-CNN [8] joins a CNN classifier the region proposal and within a single network by presenting a Region Proposal Network .it is one of the most crucial methods and widely utilized methods for object detection, which have achieved excellent results in object detection like Microsoft COCO [10] and PASCAL VOC [11]. It consists of 2 modules: a detector module and a Region Proposal Network (RPN). The RPN in component in Faster R-CNN is a fully convolutional neural network which generates object proposals. Which is that predicting bounding boxes with respect to reference boxes of multiple sizes. Another crucial thing we used convolutional network frameworks Caffe. While Caffe is most suitable choice mostly because original Faster R-CNN was implemented using Caffe, in our paper. For object classification There are several case of work on the general issue. For instance, the Pascal Visual Objects Challenge (VOC) that use the VOC dataset it consists 20 different classes also faster R-CNN [3] trained on it. we explain some object detection methods which is based to CNN.
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Learning to Relate from Captions and Bounding Boxes

Learning to Relate from Captions and Bounding Boxes

Most similar to our current task is work in the do- main of Weakly Supervised Relationship Detection. Peyre et al. (2017) uses weak supervision to learn the visual relations between the pairs of objects in an image using a weakly supervised discriminative clustering objective function (Bach and Harchaoui, 2008), while Zhang et al. (2017b) uses a region- based fully convolutional network neural network to perform the same task. They both use <subject, predicate, object> annotations without any explicit grounding in the images as the source of weak su- pervision, but require these annotations in the form of image-level triplets. Our task, however, is more challenging, because free-form captions can poten- tially be both extremely unstructured and signifi- cantly less informative than annotated structured relations.
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Animate Object Detection Using Deep Learning

Animate Object Detection Using Deep Learning

In this paper, a new aspect has been provided by using scene viola Johns and Watson algorithm for classification of animate object and the gender detection algorithms. This proposed scheme gives near to 98% results which makes it better than previous techniques used for animate object detection and gender classification. Though it is a bit expensive and time consuming because of the use of different detection algorithm along with training algorithms which makes it robust. The result provided by this technique is directly related to the properties of the input provided. Having its applications ubiquitously, it can be applied anywhere, where one wants to recognize the face and gender of person also for the animate object detection. Example Malls, where it for checking for how many Animals and the human are visited. Also used for the Passport for photo recognizing of animals
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Object Detection and Visual Innovation using AR

Object Detection and Visual Innovation using AR

Abstract: This paper presents the implementation of applications that can detect objects and display information regarding it using reverse image search and various other AR applications. It discusses mainly about MobileNets for mobiles to detect an object. This MobileNets are based on streamlined architecture that uses depth-wise separable convolutions to build light weighted deep neural networks. This paper demonstrate the effectiveness of MobileNets across a wide range of applications and use cases of object detection.. This paper also talks about augmented reality, it is a technology which combines virtual objects and real-world environment. This paper proposes application for trying different furniture items in virtual way, measuring a plot and visualising it on 3D to place furnitures. It will eliminate the need of physically visiting the furniture store which is very time consuming activity.
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Abandoned Object Detection using SVM Classifier

Abandoned Object Detection using SVM Classifier

Security and surveillance are important issues in today’s world. Any behaviour which is uncommon in occurrence and deviates from normally understood behaviour can be termed as suspicious. This paper presents an effective approach for detecting abandoned luggage in surveillance videos. We have targeted to create a system for recognising abandoned static objects and extract new information for end-users in highly secured indoor surveillance system. The objective for this project is to design a model for detection of abandoned objects. The object detection is done by background subtraction with the help of appropriate model. Face detection is done by extracting features and can be captured using Viola-Jones algorithm. The system uses image processing technique using MATLAB.
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Object Detection in Videos using Shot Clustering

Object Detection in Videos using Shot Clustering

Moving object detection and tracking is a key challenge for designing higher-level applications as monitoring traffic behavior, video surveillance and so many other different applications. These has been extensively studied by researchers in computer vision and the ITS field [6]. Object detection is the beginning for analysis of videos. It handles segmentation of moving object from stationary background object. Owing to dynamic environmental conditions such as illumination changes, shadows and waving tree branches in the wind object segmentation is a difficult and significant problem that needs to be handled well for a robust visual surveillance system. The further processing of video analysis is tracking, which simply
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Object Detection Using Probabilistic Graph Model

Object Detection Using Probabilistic Graph Model

Costas Panagiotakis, Ilias Grinias, and Georgios Tziritas [20] proposed a framework for image segmentation which uses feature extraction and clustering in the feature space followed by flooding and region merging techniques in the spatial domain, based on the computed features of classes. They use a new block-based unsupervised clustering method which ensures spatial coherence using an efficient hierarchical tree equipartition algorithm. They divide the image into different-different blocks based on the feature description computation. The image is partitioned using minimum spanning tree relationship and mallows distance. Then they apply K-centroid clustering algorithm and Bhattacharya distance and compute the posteriori distributions and distances and perform initial labelling. Priority multiclass flooding algorithm is applied and in the end regions are merged so that segmentation results are produced.
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Object Detection in an Image using Deep Learning

Object Detection in an Image using Deep Learning

Abstract— The object detection supported deep learning is a vital application in deep learning technology, that is characterised by its robust capability of feature learning and have illusation compared with the object detection methods. The paper first makes an introduction of the methods in object detection, and expounds the methods of deep learning in object detection. Then it introduces the emergence of the object detection methods based on deep learning and elaborates the most typical methods nowadays in the object detection via deep learning. In the statement of the methods, the paper focuses on the framework design and the working principle of the models and analyses the model performance in the real-time and the accuracy of detection. Eventually, it discusses the challenges in the object detection based on deep learning and offers some solutions for reference
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Object Detection and Tracking Using Uncalibrated Cameras

Object Detection and Tracking Using Uncalibrated Cameras

Feature detection: It is impossible to match pixel by pixel in images. Many points can be located in homogeneous regions where almost no information is available to differentiate between them. Hence, it is important to use feature points which are useful for matching. Interest points like lines, edges, corners, contours etc., are used for matching. Many interest point detectors exist. Harris corner detector gives the best results according to the criteria mentioned in chapter 2.

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Detection Of Anomaly Object By Using Humanoid Robot

Detection Of Anomaly Object By Using Humanoid Robot

An eye for a human are very important for recognize and detection of an object even in a room that full of darkness. Without eyes, incidents may be happened due bumped to the obstacles, mishap or be at the unfamiliar environment. Edelman in [1] pointed out that there are some theories from researchers and scientist that explaining the human perception of objects. One of it is promote the importance of multiple model views while the others postulate viewpoint invariants in the form of shape primitives (geons) as stated by Tarr et al. and Biederman in [1]. However, from all the theories, the practical conclusion is that vision systems detecting objects in a human-like manner should use locally-perceived features fundamental tool for matching the scene content to the models of known objects. In a nutshell, this study can help or apply to a humanoid robot to have an ability to recognize an object and its position thus detect any anomalities in front of it.
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Object detection and segmentation using discriminative learning

Object detection and segmentation using discriminative learning

The segmentation algorithms using discriminative fitting functions consistently outper- form ASM and AAM by a large margin in the three experiments. The performance of ASM is improved by using discriminative boundary classifiers; however, it still falls into the local extremes because the boundary classifier is local. For the three discriminative learning approaches, the classification approach has relative poor performance due to its coarse search grid in the exhaustive search. If I use a fine search grid, the segmentation accuracy is expected to improve. It is interesting to see the performance of the algo- rithms specifically designed to train classifiers in a high-dimensional model space, such as marginal space learning [95]. The ranking approach converges to the correct solution faster than the regression approach, as indicated in the benchmarks of the first-level and the second-level refinement. The main reason might be that ranking only attempts to learn a partial ordering information in the model space, and hence its learning complex- ity is lower than regression. The learned ranking functions are more effective to guide the search algorithm to the correct solution. Verifying this conjecture is left to future work. Like all discriminative learning problems, the discriminative learning approaches suffer from the problem of overfitting, especially when the variation of training data does not cover the full variability. Furthermore, the number of sampled data points is hardly sufficient when the dimension of the model space is high. Because of these problems, the fitting function does not have the desired shape on some test data, and the local optimization algorithm fails to converge to the ground truth.
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