Recognizing human behaviors from surveillance videos using the SSD algorithm

Abstract

The aim is to better recognize human behaviors from surveillance videos. Human behavior recognition technology based on surveillance videos is researched, given the intellectual development of massive surveillance video data with full coverage. This technology builds a human behavior detection and recognition model using the new Single Shot MultiBox Detector (SSD) algorithm, which improves the recognition accuracy. The constructed model’s effectiveness is verified through comparisons with other traditional human behavior recognition algorithms via the TensorFlow framework. Results demonstrate the SSD model-based recognition algorithm’s accuracy is significantly higher than that of Direct Part Marking and Fast Convolutional Neural Network (CNN) algorithms. SSD’s average speed is 0.146\xa0s/frame, and the average accuracy on different datasets is 82.8%. If the target is close or partially occluded, the SSD algorithm can also accurately detect the central target, and the detection efficiency is twice that of the R-CNN algorithm. The algorithm proposed has a simple structure and fast processing speed, which can solve the problems in target detection. The research results can provide a theoretical basis for the research on target detection related to human behavior recognition.