Weihong Yin

Stationary target detection using the objectvideo surveillance system

Detecting stationary objects, such as an abandoned baggage or a parked vehicle is crucial in a wide range of video surveillance and monitoring applications. ObjectVideo, the leader in intelligent video software has been deploying commercial products to address these problems for the last 5 years. The ObjectVideo VEW and OnBoard system addresses these problems using an array of algorithms optimized for various scenario types and can be selected dynamically. This paper describes the key challenges and algorithms, and presents results on the standard i-LIDS dataset.

Video Surveillance Using a Multi-Camera Tracking and Fusion System

Use of intelligent video surveillance (IVS) systems is spreading rapidly in a wide range of applications. In most cases, even in multi-camera installations, the video is processed independently in each feed. This chapter describes a real-time system that fuses tracking information from multiple cameras, thus vastly expanding the capabilities of IVS by allowing the user to define rules on the map of the whole area, independent of individual cameras. The fusion relies on all cameras being calibrated to a site map while the individual sensors remain largely unchanged. We present a new method to quickly and efficiently calibrate all cameras to the site map, making the system viable for large-scale commercial deployments. The method uses line feature correspondences, which enable easy feature selection and provide a built-in precision metric to improve calibration accuracy.

Fast Crowd Density Estimation in Surveillance Videos without Training

Crowd analytics is becoming a highly desirable feature of Intelligent Video Surveillance (IVS) applications. In this paper we propose a new, practical approach that adds very little computational and configuration overhead to an IVS system. The approach extends a standard IVS system, using available video content analysis data and camera calibration information to provide accurate human count estimation in crowded scenarios. The algorithm is viewpoint independent and requires no training for different camera views. The primary output of the algorithm is a real-time crowd density measurement at each image location. This can be further used to detect various crowd related events. Extensive experiments show that the approach is robust and it has been integrated into a commercially available IVS system.

Embedded smart sensor for outdoor parking lot lighting control

In this paper, we present an embedded real-time parking lot monitoring video surveillance system, automatically dimming and brightening the illumination depending on whether people or vehicles are detected in the monitored area. The system helps saving money by significantly reducing energy consumption, while maintaining appropriate levels of illumination for basic safety.