Real-Time Violence Detection in Videos Using Dynamic Images

The problem of violence detection consists of identifying scenes that characterize violence in a video stream. The violent actions in question can be of the most diverse, from fights, pushes, and robberies to shots and explosions. Detecting the presence of violence is useful for classifying videos and films, blocking inappropriate content for specific audiences, and improving security personnel's performance responsible for areas under surveillance. This work proposes an approach based on the Dynamic Images method, using handcrafted and CNN features the Bag of Visual Words paradigm and a SVM classifier to detect violent actions that involve corporal struggle in the video streams of databases of literature. The proposed methods can achieve an average accuracy of 97.50% for the Hockey dataset, 99.80% for the Movies dataset, and 93.40% for the Crowd dataset. Besides, the identification of violence in each video was performed in of hundredths of a second. Also, the techniques proposed in this work have the advantage that they can be applied even in environments where computational resources are limited, and technologies such as GPU or parallel processing are not available.