Surveillance Scene Segmentation Based on Trajectory Classification Using Supervised Learning

Scene understanding plays a vital role in the field of visual surveillance and security where we aim to classify surveillance scenes based on two important information, namely scene’s layout and activities or motions within the scene. In this paper, we propose a supervised learning-based novel algorithm to segment surveillance scenes with the help of high-level features extracted from object trajectories. High-level features are computed using a recently proposed nonoverlapping block-based representation of surveillance scene. We have trained Hidden Markov Model (HMM) to learn parameters describing the dynamics of a given surveillance scene. Experiments have been carried out using publicly available datasets and the outcomes suggest that, the proposed methodology can deliver encouraging results for correctly segmenting surveillance with the help of motion trajectories. We have compared the method with state-of-the-art techniques. It has been observed that, our proposed method outperforms baseline algorithms in various contexts such as localization of frequently accessed paths, marking abandoned or inaccessible locations, etc.