A CNN-LSTM based framework for object detection and occlusion-handling in video surveillance

Accurate object detection with occlusion handling in a dynamic and densely populated environment is a challenging issue of video surveillance system. A novel hybrid deep learning framework combining CNN (Convolutional Neural Networks) with LSTM (Long Short-Term Memory) networks for addressing this challenge of object detection and tracking over time is introduced. CNN is applied for extracting rich spatial features from individual video frames whereas LSTM is used to capture temporal dependency across sequential frames results for more accurate tracking and occlusion recovery in real-time scenario in the proposed hybrid model. The benchmark surveillance datasets PETS and MOT17 are used to validate the performance of the proposed hybrid model which results the better performance as compared with traditional approaches and standalone deep learning models in terms of detection accuracy, tracking failures and occlusion handling having complex scenes with frequent object overlap, varying motion patterns and inconsistent lighting conditions. The proposed hybrid model is scalable and adaptive nature for intelligent surveillance applications such as crowd monitoring, public safety, automated security system etc. The integration of spatial-temporal learning makes it more robust for real-time deployment with enhanced detection reliability.