Real‐time recognition of pedestrian details can be very important in emergency situations for security reasons, such as traffic accidents identification from traffic video. However, this is challenging due to the needed accuracy of video data mining, and also the performance for real‐time video processing. Here, the authors propose a solution for fine‐grained pedestrian recognition in monitoring scenarios using deep learning and stream processing cloud computing, which is called DRPRS (deep learning‐based real‐time fine‐grained pedestrian recognition using stream processing). The authors design an improved convolutional neural network (CNN) network called fine‐CNN, which is a nine‐layer neural network for detailed pedestrian recognition. In DRPRS, a pedestrian in a surveillance video is segmented and fine‐grainedly recognised using improved single‐shot detector and several fine‐CNNs. DRPRS is supported by parallel mechanisms provided by Apache Storm stream processing framework. In addition, in order to further improve the recognition performance, a GPU‐based scheduling algorithm is proposed to make full use of GPU resources in a cluster. The whole recognition process is deployed on a big video data processing platform to meet real‐time requirements. DRPRS is extensively evaluated in terms of accuracy, fault tolerance, and performance, which show that the proposed approach is efficient.
Deep learning‐based real‐time fine‐grained pedestrian recognition using stream processing
IET Intelligent Transport Systems ; 12 , 7 ; 602-609
2018-09-01
8 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
image segmentation , GPU‐based scheduling algorithm , deep learning‐based real‐time fine‐grained pedestrian recognition , traffic video , surveillance video , neural nets , big video data processing platform , cluster computing , improved single‐shot detector , fine‐CNN , DRPRS , image recognition , cloud computing , stream processing cloud computing , learning (artificial intelligence) , video surveillance , pedestrians , traffic accidents identification , real‐time video processing , parallel processing , improved convolutional neural network , video data mining
Deep learning-based real-time fine-grained pedestrian recognition using stream processing
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