We have implemented pedestrian detection using Adaptive median filter and classification using CNN network. The sliding window approach is used for region proposal and both CNN networks like AlexNet and 18 layer ResNet are used for feature detection with the softmax function acting as a classifier. The problem is formulated as single person or group of person classification. This system gives optimum results with great accuracy over Caltech and PET pedestrian data set . The model works moderately well for cases where people are undoubtedly and clearly present in the frame but does not work properly during occlusion condition and when the pedestrians are very small in size (away from the camera). The next step is post processing step which covers all the classified objects with bounding boxes. Experimental results shows that this proposed system gives optimum classification results over small dataset used. In future, the next step is to perform training on a much larger training set and also testing the performance on a larger test set. Moreover, in order to overcome the difficulty occurred because of too many samples required for implementing the sliding window approach, R-CNN approach is selected which eliminate the use of sliding window as feature detector.
The Faster-R-CNN candidate region was generated using the Region Proposal Network (RPN) instead of the Selective Search (SS) of the Fast R-CNN. Creatively adopting the RPN to generate proposal areas, and sharing the convolutional network with the target detection network, the proposed region is reduced from 2,000 to 300, and the quality of the proposed area has also been substantially improved. Algorithm flow is as follows:
Vehicle detection plays an important role in autonomous driving systems. Recently, vehicle detection methods achieved large successes with the fast development of deep convolutional neural network (CNN). However, due to small size, heavy occlusion or truncation of vehicle in an image, recent CNN detectors still show a limited performance. This paper presents an improved framework based on deep CNN for vehicle detection. Firstly, deconvolutional modules are added at multiple output layers of the reduced VGG 16 architecture to enhance additional context information which is helpful to improve the detection accuracy. Secondly, region proposal modules are applied at different feature maps to address the vehicle occlusion challenge. Due to heavy object occlusion in test dataset, soft Non-Maximum Suppression (NMS) algorithm is used to solve the issue of duplicate proposals. Finally, a deep CNN-based classifier including a region of interest (ROI) pooling layer and a fully connected (FC) layer is used for classification and bounding box regression. The proposed method is evaluated on the KITTI vehicle dataset. Experimental results show that the proposed method achieves better performance compared to other the state-of-the-art approaches in vehicle detection.
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Mask R-CNN starts with the process of sorting and refining the anchors. A positive and a negative anchor is proposed around every object which is then refined further. These anchors can be customized according to the kind of objects one wants to detect. Furthermore, these anchors play a significant role in the overall performance of Mask R-CNN. This is followed by the appearance of bounding boxes/regions as a result of the region proposal network. This is a very crucial point because region proposal network decides whether a particular object is a background or not and likewise bounding boxes are marked. The objects in the frame are determined by a classifier and a regressor by region of interest pooling. After refining of bounding boxes, masks are generated and placed on their appropriate positions on the object. This is the main distinguishable attribute of Mask R-CNN.
First, we validate the performance of different feature vectors obtained from the different Vgg16 convolutional layers. Since we just want to know the relative performance of various feature vectors, as a result, we randomly chose the 10000 negative regional proposals and 10000 positive regional proposals to conduct our experiments. The positive and negative region proposals are defined by the following way: if the overlap rate between the regional proposal and any ground truth is bigger than 0.8, this region proposal is marked as positive; if the rate is smaller than 0.5, it is marker as negative. For the case whose overlap rate is between 0.5 and 0.8, we just abort them, because we think they are neutral and it is hard to correctly label them. The TABLE Ⅱ shows the overall accuracy of all the feature vectors.
Deep learning methods are widely utilized in numerous issue areas, including object detection. Deep learning models achieved better results when trained on more data. It has ability to localize and detect objects in images. Deep learning techniques in computer vision have reached good results (Krizhevsky et al., 2012) in speech recognition. The most important field used very widely in deep learning is convolution neural network  for image detection, recognition, localization and segmentation. Convolutional networks have a great role in the history of deep learning . Convolutional networks were additionally a portion of the ﬁrst neural networks to solve the most crucial applications in commercial area and stay at the highest of commercial area applications of deep learning. In 2013, Girshick et al. published a new why  to achieve good results in object detection. This why is called R-CNN (“CNN with region proposals”). Extract features are used from each region proposal in convolutional network. R-CNN is a crucial method that provided practical solution for using object detection, but there were some problems for training, this method need multiple stages and also training for SVM and region proposal is expensive, because it need a lot of locations in disc, also another problem for detecting objects was that about a minute was required.
2) SSD: Single Shot Multibox Detector (SSD)  strives to obtain a balance between speed and accuracy. It was designed to implement object detection in real time. It’s faster than Faster R-CNN but not as accurate while it’s more accurate than YOLO but not as fast. In YOLO, the aspect ratios of bounding boxes are fixed, while SSD uses anchor boxes of different aspect ratios. This technique is similar to that of Faster R-CNN. Instead of using a region proposal network small convolution filters are used to compute object location and class. For large objects the accuracies of SSD and Faster R-CNN are comparable but as the object size decreases SSD’s accuracy drops significantly.
Traffic sign detection plays an important role in intelligent transportation systems. This paper proposes a new method for detecting small-sized traffic signs based on deep learning. MobileNets architecture is adopted as the base network to provide a rich and discriminative hierarchy of feature representations. A deconvolutional module is then integrated into Faster R-CNN framework to bring additional context information which is helpful to improve the detection accuracy for small-sized traffic signs. Additionally, atrous convolution is used in the region proposal network to enlarge the receptive field of the synthetic feature map. The proposed framework is trained and evaluated on German traffic sign detection benchmark. The results show that the proposed approach obtained an accuracy comparable to other the state-of-the-art approaches in traffic sign detection.
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If we view gland borders as a “resource” associated with image locations, then once a true gland region “consumes” this resource, a nearby artifact region could become lack of gland borders (i.e., no gland border is available for form- ing its closed boundary as a gland). Based on this obser- vation, we propose a re-weighting procedure to re-weight the region probability map generated by the regression. The purpose of the re-weighting process is to further highlight true gland regions and weaken artifact ones. We use the boundary probability map BPM again here to help the re-weighting. In Fig. 10, the gland border resource is represented by BPM, and an artifact region R is sur- rounded by 6 true gland regions. In the order of the scores assigned by the regression model (from high to low), the 6 true gland regions can take the gland border resource away from the outer contour of R 2 (the red contour in the
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Planning is an important step in development process. Keeping this in mind, Government of India enacted the Planning Board Act in 1985 with the concurrence of the constituent states of Haryana, Rajasthan and Uttar Pradesh. Union Territory of Delhi, a few ring towns around it were conceived for being developed as a Metropolitan Region i.e. National Capital Region to reduce the population pressure on Delhi and to bring about harmonized and balanced pattern of development.Thus, the motive was to shift the poly-nodal model of development from mono-nodal model of development. Various functional plans were prepared under Regional Plan-2001. But, Regional Plan-2001 failed to deliver the desired model. National Capital Region has not been able to come up as poly-nodal or multi node development region.
Design This will be a cross-sectional study that will run for a period of 3 months. Pregnant women seeking ANC or delivery services at primary (The Mankon Medical- ised Health Centre, Nkwen Medicalised Health Centre, District Hospital Santa) and secondary healthcare (The Regional Hospital Bamenda) facilities in the northwest region of the country will be recruited by a consecutive sampling. The Regional Hospital Bamenda is a second level hospital in the North west region which acts like a referral hospital in the region. It has approximately 300 outpatient consultations a day. Recruitment will be done in a consecutive manner in all four health care facilities until the sample size is attained.
This separation concept has provoked copious discussions between the stake- holders. It appears that the initial proposal on the table did not provide for the above-mentioned exceptions granting the possibility for integrated undertakings to own both sides of the rail system, though with the relevant guarantees. Bundling is perceived by the Commission and some governments as facilitating discrimination leading to the possibility that infrastructure managers, who are mainly state-owned entities, cross-subsidize the commercial activities, for instance, by diverting state funds. 80 However, separation is not itself a guarantee for competition, regulatory bodies should be appointed to enforce the implementation of the legislation. A typical example of how this can happen in practice is that of the SNCF being fined in December 2012 for anticompetitive behaviour – using confidential information for commercial purposes and attempting to prevent competitors from accessing the freight and other markets through predatory pricing. Also, the possibility given to Member States to prevent integrated operators from entering the market without the clearance of the Commission is considered to be a possible deterrent to inves- tors. 81
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The aim of the study was to seek a theologically sound, biblically grounded and sociologically appropriate means of organizing social care for the Ghana Baptist Convention (GBC) member churches in the Ashanti Region of Ghana. The absence of formal social support, amidst severe social welfare challenge has led to the emergence of several mutual, self-help societies, including the social welfare schemes of the GBC churches. Using the Zerfass (1974) practical theological model as a primary tool for the study, the research showed that the current social welfare system of the church lacks a distinctive Christian identity. Relying on an exegesis of four anchor texts to discover the standards of God, the study made proposals to address the identified deficiencies of social welfare in the churches. This study could serve also as template for other Christian communities especially in Africa.
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the south of Poland, Colluvisol with A horizons exceeding 4 m was found (Schmitt & Rodzik 2006). The bright coloured spots occurring on the base of the slope do not indicate soil loss but rather the accumulation of mineral loess mate- rial (slightly increased carbonate content in the plough layer of Colluvisol; Figure 4b). Terhorst (2000) identifies similar calcareous colluvial layers on the valley floors in the south-west Germany loess region. This means that the Colluvisol itself is covered by non-humic material in some parts of the slope, which results in subfosilisation of the soil profile and retrograded soil development.
Abstract: Recent studies by Section of Social and Economic events, United Nations evaluate, today 55% world’s citizenry lives in metropolitan region and percentage is that assume to rise 68% by 2050. When a city grows, peripheral region also develops in haphazard mode. The unmanaged density and land use become crucial issues for civic authorities for provision of infrastructure and planners to plan. If the area already included in urban planning well before to accommodate future growth, the management of land issues can be resolved. The first step of the study is to acquire multi temporal data to detect the changes over the time. For this purpose, Landsat – 5&8 TM (1991-2018) (Source: USGS Earth Explorer), satellite images have been acquired. For this, delineation is done with the help of GIS software, to propose Nashik Regional Development Authority. With this process of delineation regional level development, it can be controlled and covered under a unified regulatory framework with a well-planned land use zoning to accommodate further expansion. Major parameters of this study are to analyze population density and growth pattern and to identify the future expansion factors which are accountable for growth of Nashik city. As a result of urbanization and expansion of municipal corporation limit, the city has undergone a drastic change in land use character. The study has analyzed the relationship between urban expansion using remote sensing and GIS.
continuously which classification and regression use the multitasking loss layer to improve the accuracy of the algorithm. However, there are still many areas to be identified, and this part of the algorithm is temporarily unable to integrate into the GPU, and the efficiency of the whole algorithm is still not high. Based on the assumption that candidate frame extraction can be performed on the feature map, Shaoqing Ren et al. proposed the Faster R-CNN  algorithm, which introduces a new concept, the regions proposal network(RPN). In the RPN network design for generating the proposal window, Faster-RCNN  implements the extraction of the candidate frame for the object classification and the frame regression by sliding the window on the feature map. The object detection and recognition of Faster-RCNN  are integrated into a network. The whole model is an end-to-end process, which dramatically improves the overall performance, especially concerning detection speed.
The contribution deal with problems of modelling solution of place to biowaste composting and it´s optimum placing, applied on concrete conditions of the Lednice-Valtice Area. The basis is placement of dominant producer of biowaste, their kind, quantity and season in relation to prescription of compost fill. The proposal of compost technology enable determine size of place and help solve its placing. Circumscribed method is able to find practical exploitation at creation of place suggestion in real con- dition of existent areas.
We propose a new approach to evaluate the leading-order hadronic contribution to the muon anomaly. The direct measurement of the hadronic contribution to anomalous muon magnetic moment will provide an independent determination, competitive with the time-like dispersive approach, and consolidate the theoretical prediction for the muon g-2 in the Standard Model. It will allow a ﬁrmer interpretation of the measurements of the future muon g-2 experiments at Fermilab and J-PARC. This new approach may become a path within an unexplored region of the ﬁeld theoretical dy- namics. It may lead to a possibly long series of phenomenological results. The (crossed) t-channel dynamics, as complementary and independent with respect to the s-channel one, will permit an alternative new approach to the Standard Model precision physics.
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