A Research On Pedestrian Detection - 1896 Words

The four papers about pedestrian detection we chose to summarize were great and informative, all suggested useful techniques and new ideas in deep learning for pedestrian detection. However, there were few open issues or room for improvement in some of the papers. Here are some of the ideas we suggested to resolve these issues in each paper. Joint Deep Learning for Pedestrian Detection (UDN) Even though the Unified Deep Net (UDN) method learned features by designing hidden layers for the Convolutional Neural Network such that features, deformable parts, occlusions, and classification can be jointly optimized, one of its problems is it treats pedestrian detection as a single binary classification task, which is not able to capture rich pedestrian variations. For example, the method is not able to distinguish pedestrians from hard negatives due to their visual similarities. This problem can be resolved by jointly optimizing pedestrian detection with auxiliary semantic tasks, such as including pedestrian attributes and scene attributes, which was represented in our previous report. Another problem with the UDN method is it did not explicitly model mixture of templates for each body parts, and did not depress the influence of background clutters. Thus, the method could be improved by explicitly model the complex mixture of visual appearance at multiple levels. For example, some extra layers can be added into the hierarchy of the UDN, so that at each feature level, thisShow MoreRelatedA Brief Note On Pedestrian Detection Of Surveillance1163 Words   |  5 PagesPedestrian Detection in Surveillance Abstract Pedestrian detection is fragmented as there are numerous algorithms used in different research. 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