To enhance the quality of organizing and implementing technology project review meetings and to regulate participants'behavior during the review process,a behavior analysis method based on an improved YOLOv5 is proposed.This method enables real-time analysis of surveillance video data from review meetings to identify participants'violations.First,an improved YOLOv5-based small-object detection network is constructed for monitoring video data.By integrating the TCANet attention mechanism into the YOLOv5 backbone network,the model focuses on key target areas within the surveillance footage of review meetings.Additionally,the head network incorporates an upsampling process,where the upsampled feature maps are fused with shallow feature maps from the backbone network to achieve detection of small objects such as mobile phones and business cards in the meeting environment.Next,a participant behavior analysis algorithm is proposed.Using a human target tracking network model,the system tracks participants'movement trajectories in real time.A spatiotemporal correlation model is established by combining regional attributes with the spatial domain of expert locations,enabling the detection of participant behaviors,such as interactions and conversations with experts,which may constitute violations.Experimental results demonstrate that the method achieves a detection accuracy of 0.657 for small objects like mobile phones and business cards,with a mAP improvement of 0.196 compared to YOLOv5m.The participant tracking accuracy reaches 0.938,with an image processing frame rate of 21 frames per second(F/s).This approach effectively identifies participant behaviors such as contact and conversation,making significant contributions to the intelligent management of participant behavior during review meetings.
维普
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