This work presents a method for detecting abandoned objects in real-world conditions. The method presented here addresses the online and real time aspects of such systems, utilizes logic to differentiate between abandoned objects and stationary people, and is robust to temporary occlusion of potential abandoned objects. The capacity to not detect still people as abandoned objects is a major aspect that differentiates this work from others in the literature. Results are presented on 3 hours 36 minutes of footage over four videos representing both sparsely and densely populated real-world situations, also differentiating this work from others in the literature

This paper presents algorithms for vision-based detection and classification of vehicles in monocular image sequences of traffic scenes recorded by a stationary camera. Processing is done at three levels: raw images, region level, and vehicle level. Vehicles are modeled as rectangular patches with certain dynamic behavior. The proposed method is based on the establishment of correspondences between regions and vehicles, as the vehicles move through the image sequence. Experimental results from highway scenes are provided which demonstrate the effectiveness of the method. We also briefly describe an interactive camera calibration tool that we have developed for recovering the camera parameters using features in the image selected by the user.