A Novel Approach for Robust Multi Human Action Recognition and Summarization based on 3D Convolutional Neural Networks
This addresses the challenge of searching for specific actions or persons in long surveillance videos, though it appears incremental as it builds on existing 3DCNN techniques.
The paper tackles the problem of recognizing and summarizing multiple human actions in videos, particularly for surveillance, by proposing a method that extracts individual sequences and uses 3D convolutional neural networks, achieving accurate results compared to state-of-the-art methods.
Human actions in videos are 3D signals. However, there are a few methods available for multiple human action recognition. For long videos, it's difficult to search within a video for a specific action and/or person. For that, this paper proposes a new technic for multiple human action recognition and summarization for surveillance videos. The proposed approach proposes a new representation of the data by extracting the sequence of each person from the scene. This is followed by an analysis of each sequence to detect and recognize the corresponding actions using 3D convolutional neural networks (3DCNNs). Action-based video summarization is performed by saving each person's action at each time of the video. Results of this work revealed that the proposed method provides accurate multi human action recognition that easily used for summarization of any action. Further, for other videos that can be collected from the internet, which are complex and not built for surveillance applications, the proposed model was evaluated on some datasets like UCF101 and YouTube without any preprocessing. For this category of videos, the summarization is performed on the video sequences by summarizing the actions in each subsequence. The results obtained demonstrate its efficiency compared to state-of-the-art methods.