W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principal Axis-Based Correspondence between Multiple Cameras for People Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
KNIGHT/spl trade/: a real time surveillance system for multiple and non-overlapping cameras
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
People detection and tracking using stereo vision and color
Image and Vision Computing
Robust tracking with motion estimation and local Kernel-based color modeling
Image and Vision Computing
Automated Visual Surveillance in Realistic Scenarios
IEEE MultiMedia
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast and automatic video object segmentation and tracking for content-based applications
IEEE Transactions on Circuits and Systems for Video Technology
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Using the global positioning system (GPS) for people tracking continues to get easier. A person can transmit his/her GPS location from the carried mobile devices. The location is usually displayed as a dot on a digital map. However, a dot on the map is insufficient to reveal the person's actual situation, e.g., an accident being happening. If the GPS is incorporated with an IP (Internet Protocol) camera, the camera image is critical in revealing the person's actual situation and to improve the above-mentioned insufficient information. We present an approach to facilitate such incorporation. The approach consists of three phases: locating, tracking and monitoring collision. When the GPS coordinates of a person are within the field-of-view (FOV) of a camera, the approach enters the locating phase. The GPS coordinates are transformed to specify a candidate area (CA) in the image. The update of GPS coordinates is used to filter those moving objects within the CA until only one remains. After the person is located, he is being tracked using the shortest Euclidean distance method to find the most likely object in the next image. If the person collides with other objects while being tracked, a template matching technique, the sum of absolute difference (SAD), is used to locate the person in the collision area. The tracking is done after the person leaves the FOV of the camera. In the experimental studies, the tracking of one to three persons was performed using the implemented prototype. The average locating error of the tracking phase is only 5 pixels. The highest and average tracking success rates are 95.9% and 90.6%, respectively. These results show that the proposed approach is accurate and feasible for people tracking by incorporating GPS and IP cameras.