Efficient Semantic Enrichment Process for Human Trajectories in Surveillance Videos Nowadays, it becomes very convenient to collect largescale videos that record trajectories of human mobility behavior in various situations in cities, due to the increasing availability of surveillance camera. Obviously, surveillance videos became a new data source of spatiotemporal trajectories. However, a typical trajectory semantic enrichment process receives as input spatiotemporal trajectories. The process methods cannot be applied to video data directly. In this paper, we propose a semantic enrichment process framework for human trajectories in surveillance videos. It includes trajectory identification in videos, trajectory transformation, subtraj ectory segmentation, segment annotation. We can derive semantic trajectories from surveillance videos through the four phases. Having observed the common occurrence of the similarities between individual trajectories, we propose a grid indexbased method to search similar preannotated subtrajectory segments in pixel space for retrieving semantic trajectories in order to enhance the performance of this approach. Finally, we demonstrate the effectiveness and efficiency of our proposed approach by using a real world data set.

Tracking people in mixed modality systems In traditional surveillance systems tracking of objects is achieved by means of image and video processing. The disadvantages of such surveillance systems is that if an object needs to be tracked  it has to be observed by a video camera. However, geometries of indoor spaces typically require a large number of video cameras to provide the coverage necessary for robust operation of videobased tracking algorithms. Increased number of video streams increases the computational burden on the surveillance system in order to obtain robust tracking results. In this paper we present an approach to tracking in mixed modality systems, with a variety of sensors. The system described here includes over 200 motion sensors as well as 6 moving cameras. We track individuals in the entire space and across cameras using contextual information available from the motion sensors. Motion sensors allow us to almost instantaneously find plausible tracks in a very large volume of data, ranging in months, which for traditional video search approaches could be virtually impossible. We describe a method that allows us to evaluate when the tracking system is unreliable and present the data to a human operator for disambiguation.