Real Surveillance Scenarios Research Articles

Human Activity Clustering for Online Anomaly Detection

This paper aims to address the problem of profiling human activities captured in surveillance videos for the applications of online normal human activity recognition and anomaly detection. A novel framework is developed for automatic human activity modeling and online anomaly detection without any manual labeling of the training dataset. The framework consists of the following key components: 1) A compact and effective activity representation method is developed based on a stochastic sequence of spatiotemporal actions. 2) The natural grouping of activities is discovered through a novel clustering algorithm with unsupervised model selection. 3) A runtime accumulative anomaly measure is introduced to detect abnormal activities, whereas normal human activities are recognized when sufficient visual evidence has become available based on an online Likelihood Ratio Test (LRT) method. This ensures robust and reliable anomaly detection and normal activity recognition at the shortest possible time. Experimental results demonstrate the effectiveness and robustness of our approach using noisy and sparse datasets collected from a real surveillance scenario.

Performance analysis for automated gait extraction and recognition in multi-camera surveillance

Many studies have confirmed that gait analysis can be used as a new biometrics. In this research, gait analysis is deployed for people identification in multi-camera surveillance scenarios. We present a new method for viewpoint independent markerless gait analysis that does not require camera calibration and works with a wide range of walking directions. These properties make the proposed method particularly suitable for gait identification in real surveillance scenarios where people and their behaviour need to be tracked across a set of cameras. Tests on 300 synthetic and real video sequences, with subjects walking freely along different walking directions, have been performed. Since the choice of the cameras’ characteristics is a key-point for the development of a smart surveillance system, the performance of the proposed approach is measured with respect to different video properties: spatial resolution, frame-rate, data compression and image quality. The obtained results show that markerless gait analysis can be achieved without any knowledge of camera’s position and subject’s pose. The extracted gait parameters allow recognition of people walking from different views with a mean recognition rate of 92.2% and confirm that gait can be effectively used for subjects’ identification in a multi-camera surveillance scenario.