Moving Object Detection

Keywords-Background Subtraction, Motion Detection, Neural Network, Self Organization, visual surveillance. I INTRODUCTION Moving object detection and tracking algorithms arean important research area of computer vision and comprise building blocks of various high-level techniques in video analysis that include tracking and classification of trajectories. In video surveillance system, detection and tracking of object is lower level task that provides support to higher level tasks such as event detection. Categorizing moving objects is a critical task which requires video segmentation, which is used in number of computer vision applications such as video surveillance, traffic monitoring, and remote sensing There are three major steps in video surveillance analysis: detection of moving objects, tracking of interested objects from consecutive frames, and the third is analysis of these tracked objects to identify its behavior, and also to identify normal/abnormal events. In this paper we are using two different methods for moving object detection.[1][3]

Index Terms—moving object detection, aerial video, tracking, real-time video processing, detection-by-tracking 1. INTRODUCTION Moving object detection is one of the most challenging problems in computer vision, especially for the aerial videos, which are captured by the Unmanned Aerial Vehicles (UAV). In recent years, the UAV is a growing field with military and civilian applications, including surveillance, rescue, and reconnaissance. In contrast to applications with fixed cameras, such as traffic monitoring and building surveillance, aerial surveillance has the advantages of higher mobility and larger surveillance scope.

Audio Assistance for Moving Object Detection Anuj Mandhare, Megha Tufchi, Nisha Koul, Rahul Singh, Prof. V.V. Waykule Dept. of Computer, AISSMS COE, Pune, India ABSTRACT: The goal of the project is to recognize moving, stationary object, track them throughout their life spans and provide Audio output for detected objects to the specially disabled people. Our algorithm uses a combination of motion detection and image-based template matching to track the targets. Motion detection is determined by temporal conferencing and template matching is done only on the locations as guided by the motion detection stage to provide a robust target-tracking method. Results show robust object recognition in some tested images, perfect tracking for the input.

LITERATURE REVIEW Moving object detection can be divided in two categories, moving object detection from stationary platform and moving platform. Different approaches including hybrid algorithms have been investigated for stable platform. One of the popular methods that are used to detect the moving objects from stationary platform is adaptive background subtraction. Some researches [3,4] tried to prepare different kinds of this algorithm with various updating rules. The main drawback of this algorithm is missing moving objects in the scene that is just starting to move.

Keywords - Background subtraction, Frame differencing, Mixture of Gaussian, Moving object detection. I. I NTRODUCTION The detection of an object in a video camera scene is a relatively new research area in computer science and, because of its broad applicability in real life this has been growing more and more. The CCTV is one of the main reasons for the growing interest and use of video in security systems. Moving object detection in a video stream is an essential step in video surveillance applications. In some algorithms, the moving objects may become part of the scene when they come to a stop. Also the scene maybe affected by changes in the light, leaves swaying, cameras shaking, etc. Many algorithms for moving object detection have been proposed in recent years. These involve background subtraction, optical flow, temporal difference and many other algorithms for detecting moving objects. From these, the most widely used algorithm is background subtraction which has many algorithms such as frame difference, approximate median, Gaussian mixture.

Volume 2, Issue 2, April 2014 Abstract— The analysis of human body motion is an important method in which computer vision combines with bio- mechanics. This method is widely used in motion detection, motion analysis, intelligent control and many other fields. In the analysis of human body motion; the moving human body detection is important part. The moving human body is detected from the background image in video sequences. Here the new method for the moving object detection based on background subtraction is defined by establishing a reliable background updating model which uses a dynamic optimization threshold method to obtain a more complete moving object. After getting moving object to remove the noise morphological filtering is done. The noise is in form of disturbances which present in the background. For removing the effect of shadow contour projection analysis is combined with the shape analysis, so that moving human body detection is done more accurately and reliably. The Background Subtraction method is accurate, faster and fits in detecting real time environment.

KEYWORDS: Background subtraction, Gaussian blur, Otsu thresholding, Shadow removal, Morphological operation, Moore neighborhood tracing, and video summary. I. I NTRODUCTION The security and safety are major concern in present era. The organization put a lot of resources and wealth on security and surveillance. Due to that there is a need for the surveillance system which are cost and application efficient. Traditional method used manpower for surveillance, but with the need of 24 hours security and surveillance, the camera surveillance system comes in market. Motion detection in video is nothing but the detecting of moving object per frame.In video surveillance system, moving object detection is the capability of the system to detect motion between two frames. For this a fixed base camera has been placed and is set as an observer at the outdoor for surveillance. Threshold value defined for moving object and any small movement beyond that threshold will be consider as the motion.

Abstract In computer vision, machine learning and pattern recognition, moving object de- tection has always been a popular research direction, and has received extensive attention of academia and industry. Moving object detection is mainly to detect the foreground moving objects in the scene by using the video sequences, including the walking pedestrians, driving vehicles, moving boats and so on. The moving object detection not only can be directly used in practical scene, but also can provide the ba- sis for video post-processing, including object recognition, object tracking, behavior analysis and so on. Therefore, moving object detection has important research signif- icance and practical value in intelligent monitoring and intelligent human-computer interaction and other applications. However, due to the video sequences from the real scene, there are many interference factors. Thus, moving object detection technol- ogy faces many challenges. On the other hand, subspace learning is a very popular research topic in recent years, which can quickly and accurately analyze the data by reducing the high-dimensional data to low-dimensional subspace. Therefore, the study of the subspace learning technology based moving object detection is mean- ingful both in theory and practical aspect. In this paper, the main research work is as follows:

This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/ Abstract Moving object detection including background subtraction and morphological processing is a crit- ical research topic for video surveillance because of its high computational loading and power consumption. This paper proposes a hardware design to accelerate the computation of back- ground subtraction with low power consumption. A real-time background subtraction method is designed with a frame-buffer scheme and function partition to improve throughput, and imple- mented using Verilog HDL on FPGA. The design parallelizes the computations of background up- date and subtraction with a seven-stage pipeline. A stripe-based morphological processing and accounting for the completion of detected objects is devised. Simulation results for videos of VGA resolutions on a low-end FPGA device show 368 fps throughput for only the real-time background subtraction module, and 51 fps for the whole system, including off-chip memory access. Real-time efficiency with low power consumption and low resource utilization is thus demonstrated.

4. Comparison and discussion In this article, we have proposed a new method for moving object detection using a keypoint model and compared it to the GMM [1,2,5,10], which is considered to be one of the best BGS models available. The Intel (R) core (TM) i7- 960 @ 3.2 GHz CPU with 5 GB RAM is chosen as the hardware platform. Algorithm implementation is done using a C-based computer vision library, “OPENCV,” to carry out real-time performance for these two models.

ABSTRACT Detection and segmentation of objects of interest in image sequences is the first major processing step in visual surveillance applications. The outcome is used for further processing, such as object tracking, interpretation, and classification of objects and their trajectories. To speed up the algorithms for moving object detection, many applications use techniques such as frame rate reduction. However, temporal consistency is an important feature in the analysis of surveillance video, especially for tracking objects. Another technique is the downscaling of the images before analysis, after which the images are up-sampled to regain the original size. This method, however, increases the effect of false detections. We propose a different pre-processing step in which we use a checkerboard-like mask to decide which pixels to process. For each frame the mask is inverted to avoid that certain pixel positions are never analyzed. In a post-processing step we use spatial interpolation to predict the detection results for the pixels which were not analyzed. To evaluate our system we have combined it with a background subtraction technique based on a mixture of Gaussian models. Results show that the models do not get corrupted by using our mask and we can reduce the processing time with over 45% while achieving similar detection results as the conventional technique.

2 Associate Prof. Dept. of Electronics and Telecommunication Engineering, Bharati Vidyapeeth’s College of Engineering, Shivaji University, Kolhapur, India Abstract: Moving object detection from dynamic scenes has been used in many computer vision applications like face detection, video processing, video surveillance, traffic monitoring etc. Finding the position is much more challenging task than detecting the moving object in a video. Here we are presenting a brief review of various algorithms for moving object detection which are already available. Moving object detection from dynamic scenes using Multiple Color Space Histogram Model is briefly discussed in this paper. In this model, at first, convert each frame from RGB space to other color spaces and calculate the histograms of selected color components, then we can obtain the background histogram model; then, detect the objects using statistical histogram superposition principle; at last, update MCSHM by the result of detection. The experimental results demonstrate that our method can quickly and accurately detect moving objects in dynamic scenes.

The present work proposes many threshold techniques for moving object detection and tracking system. It applies more than one threshold techniques during segmentation phase of the work. Object detection is done by background subtraction with Alpha method and object tracking is carried out by feature point tracking approach. It is observed that Otsu threshold method seems to have produced a perfect extraction and yielded good result in moving object tracking. The results of applying multiple thresholds are reported in this paper.

Rozenn.Dahyot@cs.tcd.ie Abstract In this article, we consider the robust estimation of a location parameter using M- estimators. We propose here to couple this estimation with the robust scale estimate pro- posed in [Dahyot and Wilson, 2006]. The resulting procedure is then completely unsuper- vised. It is applied to camera motion estimation and moving object detection in videos.

In this work, a new structure of detection is proposed in which adaptive noise cancellation (ANC) algorithm is utilized along with local MAP estimation. Adaptive noise cancellation basically is an alternative technique for estimating the original signals corrupted by additive noise or interference. In the context of signal and image processing, ANC has been already used in works which mostly estimate an image from a version of itself contami- nated with additive noise [19-22]. In other words, it only removes the effect of noise. In this paper, ANC is exploited for moving object detection in video surveillance applications so that it eliminates noise, repeated motions of background, illumination changes, and shadows. Then, MAP estimation renders the regions corresponding to moving objects more compact and smooth. Proposed ANC-MAP method suffers no longer from heavy compu- tational complexity required in global MAP estimation.

Fig. 4 shows the schematic of our proposed PS, and the interaction between the detection and classification mod- ules. The PS aims at detecting, classifying and tracking a set of moving objects of interest that may appear in front of the vehicle. The inputs of the fusion module are three lists of detected objects from three sensors: lidar, radar and camera. Each object is represented by its position, size and an evidence distribution of class hypotheses. Class information is obtained from the shape, relative speed and visual appearance of the detections. Lidar and radar data are used to perform moving object detection and, in cooperation with image data they extract object classification. Three list of composite object descriptions are taken by our fusion approach and delivered to our tracking algorithm. The final output of the fusion method comprises a fused list of object detections that will be used for the tracking module to estimate the moving object states and deliver the final output of our DATMO solution.

A Fast Moving Object Detection Technique In Video Surveillance System Abstract— Nowadays automated surveillance system has become a trend in field of security. Video processing algorithms are utilized to implement these systems. For any video processing system, first task is to detect moving object or subtract a background. In Computer Vision, many techniques are available for detection of the moving object, but Mixture of Gaussian (MoG) models [1] is best suited for system having static and complex background with clutters. MoG technique is more accurate but has a larger time complexity which is unrealistic for real time processing. In this paper, we present a fast technique to extract moving object from background using MoG model and Haar wavelet. In this technique, before applying the MoG we down sample each video frame to acceptable resolution using Haar wavelet decomposition. Selection of wavelet decomposition level depends on original video resolution. The technique has been implemented and tested on videos of PETS [2] and CAVIAR [11] databases. For PETS sample, Original video sample frames having resolution 768 X 576 are down sampled to resolution 192 X 144 using level three Haar wavelet decomposition. Then MoG model is applied to subtract the background. Our result shows this technique is able to detect all moving objects from video in presence of complex background and clutters. We observed that this technique works almost three times faster than using only MoG model without sacrificing the quality of results.

researchers have shifted their attentions to saliency detection, and plenty of saliency-based object detection methods have been designed. Initially, saliency detection is mainly based on low-level features, e.g., edges, colors, and textures. Recently, many new measures have been adopted in this literature, such as region contrast, 9 patch rarities, 10 and difference in frequency domain. 11,12 In addition, Wang 13 used visual saliency in aerial video summarization. Besides, in order to give a further descrip- tion for moving objects, some researchers have also tried to com- bine temporal and spatial information in moving object detection. 14–17 Yin 14 used a 3D Markov random field (MRF) to predict each pixel’s motion likelihood and the message was passed in a 6-connected spatiotemporal neighborhood. As every pixel needs to be predicted by MRF, the computational cost is huge. Liu 15 introduced saliency in moving object detection.

Moving object detection are often done effectively and simply by optical flow analysis. The planned technique is analysed in numerous combos to trace the multiple objects within the screen with moving background and management some demerits of multi- object chase like appearance, disappearance and missing of the object, it provides higher accuracy in less machine time. It is very efficient in real time video surveillance system. Further work extended to detect the multiple objects with very high accuracy.

ABSTRACT Video surveillance systems have long been in use to monitor security sensitive areas. The making of video surveillance systems “smart” requires fast, reliable and robust algorithms for moving object detection, classification, tracking and activity analysis. Moving object detection is the basic step for further analysis of video. It handles segmentation of moving objects from stationary background objects. Object classification step categorizes detected objects into preened classes such as human, vehicle, animal, clutter, etc. It is necessary to distinguish objects from each other in order to track and analyse their actions reliably. In previous system performed background subtraction by using Canny Edge Detection. In Canny Edge Detection process we are taking two images for comparison those are background image and foreground image.