Real-Time Deep Learning Method for Abandoned Luggage Detection in Video
This addresses security concerns in public areas by providing real-time detection, but it is incremental as it builds on existing methods like CNNs.
The paper tackles the problem of detecting abandoned luggage in surveillance video to address security threats, achieving better performance than a strong CNN baseline method.
Recent terrorist attacks in major cities around the world have brought many casualties among innocent citizens. One potential threat is represented by abandoned luggage items (that could contain bombs or biological warfare) in public areas. In this paper, we describe an approach for real-time automatic detection of abandoned luggage in video captured by surveillance cameras. The approach is comprised of two stages: (i) static object detection based on background subtraction and motion estimation and (ii) abandoned luggage recognition based on a cascade of convolutional neural networks (CNN). To train our neural networks we provide two types of examples: images collected from the Internet and realistic examples generated by imposing various suitcases and bags over the scene's background. We present empirical results demonstrating that our approach yields better performance than a strong CNN baseline method.