Real time video pedestrian detection based on improved SSD in edge environment

MengGe Zhang; Jian Li; HuiWen Chen

doi:10.1117/12.2646916

7 September 2022 Real time video pedestrian detection based on improved SSD in edge environment

MengGe Zhang, Jian Li, HuiWen Chen

Proceedings Volume 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022); 123291G (2022) https://doi.org/10.1117/12.2646916
Event: Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 2022, Changsha, China

Abstract

Object detection is an essential subject in the field of computer vision. It is widely used in various scenes and greatly shares the work of human beings. In view of the lack of real-time performance of pedestrian detection in the field of object detection and the excessive load of data returned by the central server in the cloud computing environment, this paper proposes an improved SSD model under the edge computing architecture. The intelligent mobile terminal completes the real-time data collection, and the edge server undertakes the preliminary calculation, The cloud server is responsible for complex computing and model training. The SSD algorithm is lightweight improved to better adapt to the performance of the edge server. The idea of feature pyramid is introduced to fuse the semantic information carried by the deep feature map with the shallow feature map to improve the detection accuracy of small objects. Through experiments, the model has achieved about 0.72 map and about 0.034s single frame processing speed, which can be applied to real life.

Citation Download Citation

MengGe Zhang, Jian Li, and HuiWen Chen "Real time video pedestrian detection based on improved SSD in edge environment", Proc. SPIE 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 123291G (7 September 2022); https://doi.org/10.1117/12.2646916

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KEYWORDS

Video

Clouds

Convolution

Detection and tracking algorithms

Performance modeling

Data centers

Data modeling

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