Reasearch Topics: Human in a Surveillance Camera Network

Vision-based Localization in a camera network
	In this paper, a fully-automated person Re-ID (Re-identification) system is proposed for real scenarios of human tracking in non-overlapping camera network. The system includes two phases of human detection and Re-ID. The human ROIs (Regions of Interest) are extracted from human detection phase and then feature extraction is done on these ROIs in order to build human descriptor for Re-ID. Unlike other approaches which deal with manually-cropped human ROIs for person Re-ID, in this system, the person identity is determined based on the human ROIs extracted automatically by a combined method of human detection. Two main contributions are proposed on both phases of human detection and Re-ID in order to enhance the performance of person Re-ID system. First, an effective shadow removal method based on score fusion of density matching is proposed to get better human detection results. Second, a robust KDES (Kernel DEScriptor) is extracted from human ROI for person classification. Additionally, a new person Re-ID dataset is built in real surveillance scenarios from multiple cameras. The experiments on benchmark datasets and our own dataset show that the person Re-ID results using the proposed solutions outperform some of the state-of-the-art methods.
[1] Thi Thanh Thuy Pham, Hai Vu and Anh Tuan Pham, "A Robust Shadow Removal Technique Applying For Person Localization in a Surveillance Environment",  in the Proceeding of The Sixth International Symposium on Information and Communication Technology (SoICT 2015) , Hue, Vietnam [2] Thi Thanh Thuy Pham, Thi Lan Le, Hai Vu, Trung Kien Dao, Van Toi Nguyen, "Fully-automated person re-identification in multi-camera surveillance system with a robust kernel descriptor and effective shadow removal method", Image and Vision Computing, Elsevier, Vol. 59, pp. 44–62, Mar. 2017 [3]
Human tracking and linking trajectories in a surveillance camera network
	We propose a high accuracy solution for locating pedestrians from video streams in a surveillance camera network. For each camera, we formulate the vision-based localization service as detecting foot-points of pedestrians in the ground plane. We address two critical issues that strongly a ect the foot-point’s detection results: casting shadows and pruning detection results due to occlusion. For the rst issue, we adopt a removing shadow technique based on a learning-based approach. For the second issue, a regression model is proposed to prune the wrong foot-point detection results. The regression model plays a role in estimating the position by using the human factors such as height, width and its ratio. A correlation of the detected foot-points and the results estimated from the regression model is examined. Once a foot-point is missed due to uncorrelation problem, a Kalman lter is deployed to predict the current location. To link the trajectory of the human in the camera network, we base on an observation about the same ground-plane/ oor in view of cameras then the transformation between a pair of cameras could be computed o ine. In the experiments, a high accuracy performance for locating the pedestrians and a real-time computation are achieved. The proposed method therefore is particularly feasible to deploy the vision-based localization service in scalable indoor environments such as hall-way, squares in public buildings, o ces, where surveillance cameras are common used.
[1]  Hai Vu, Anh-Tuan Pham, Van-Giap Nguyen, Thanh-Hai Tran, "Pedestrian Localization and Trajectory Reconstruction in a Surveillance Camera Network", to appear in the Proceeding of The Eighth International Symposium on Information and Communication Technology (SoICT 2017) , Nha Trang, Vietnam [2] Thanh Thuy Pham, Anh Tuan Pham, Hai Vu, "A new technique for linking person trajectories in surveillance camera network",  in the Proceeding of Fundamental and Applied IT Research - FAIR  2015, Ha noi
Abnormality behavior detection [to be updated ...]