LAUSR

Nowadays, the video surveillance systems may be omnipresent, but essential for supervision everywhere, e.g., ATM, airport, railway station and other crowded situations. In the multi-view video systems, various cameras are producing a huge amount of video content around the clock which makes it difficult for fast browsing, retrieval, and analysis. Accessing and managing such huge data in real time becomes a real challenging task because of inter-view dependencies, illumination changes and the bearing of many inactive frames. The work highlights an accurate and efficient technique to detect and summarize the event in multi-view surveillance videos using boosting, a machine learning algorithm, as a solution to the above issues. Interview dependencies across multiple views of the video are captured via weak learning classifiers in boosting algorithm. The light changes and still frames are tackled with moving an object in the frame by Deep learning framework. It helps to reach the correct decision for the active frame and inactive frame, without any prior information about the number of issues in a video. Target, as well as subjective ratings, clearly indicate the potency of our proposed DELTA model, where it successfully reduces the video data, while keeping the important information as events.