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Human actions in videos are 3D signals. However, there are a few methodsavailable for multiple human action recognition. For long videos, it'sdifficult to search within a video for a specific action and/or person. Forthat, this paper proposes a new technic for multiple human action recognitionand summarization for surveillance videos. The proposed approach proposes a newrepresentation of the data by extracting the sequence of each person from thescene. This is followed by an analysis of each sequence to detect and recognizethe corresponding actions using 3D convolutional neural networks (3DCNNs).Action-based video summarization is performed by saving each person's action ateach time of the video. Results of this work revealed that the proposed methodprovides accurate multi human action recognition that easily used forsummarization of any action. Further, for other videos that can be collectedfrom the internet, which are complex and not built for surveillanceapplications, the proposed model was evaluated on some datasets like UCF101 andYouTube without any preprocessing. For this category of videos, thesummarization is performed on the video sequences by summarizing the actions ineach subsequence. The results obtained demonstrate its efficiency compared tostate-of-the-art methods.

A Novel Approach for Robust Multi Human Action Recognition and Summarization based on 3D Convolutional Neural Networks