Detection violent behaviors: A survey

Violence detection behavior is a particular problem regarding the great problem action recognition. In recent years, the detection and recognition of violence has been studied for several applications, namely in surveillance. In this paper, we conducted a recent systematic review of the literature on this subject, covering a selection of various researched papers. The selected works were classified into three main approaches for violence detection: video, audio, and multimodal audio and video. Our analysis provides a roadmap to guide future research to design automatic violence detection systems. Techniques related to the extraction and description of resources to represent behavior are also reviewed. Classification methods and structures for behavior modelling are also provided.European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project n ∘ 039334; Funding Reference: POCI-01-0247-FEDER-039334]. This work has been supported by national funds through FCT – Fundação para a Ciência e Tecnologia through project UIDB/04728/202

Single Shot Temporal Action Detection

Temporal action detection is a very important yet challenging problem, since videos in real applications are usually long, untrimmed and contain multiple action instances. This problem requires not only recognizing action categories but also detecting start time and end time of each action instance. Many state-of-the-art methods adopt the "detection by classification" framework: first do proposal, and then classify proposals. The main drawback of this framework is that the boundaries of action instance proposals have been fixed during the classification step. To address this issue, we propose a novel Single Shot Action Detector (SSAD) network based on 1D temporal convolutional layers to skip the proposal generation step via directly detecting action instances in untrimmed video. On pursuit of designing a particular SSAD network that can work effectively for temporal action detection, we empirically search for the best network architecture of SSAD due to lacking existing models that can be directly adopted. Moreover, we investigate into input feature types and fusion strategies to further improve detection accuracy. We conduct extensive experiments on two challenging datasets: THUMOS 2014 and MEXaction2. When setting Intersection-over-Union threshold to 0.5 during evaluation, SSAD significantly outperforms other state-of-the-art systems by increasing mAP from 19.0% to 24.6% on THUMOS 2014 and from 7.4% to 11.0% on MEXaction2.Comment: ACM Multimedia 201