No abstract

Object tracking quality usually depends on video scene conditions (e.g. illumination, density of objects, object occlusion level). In order to overcome this limitation, this article presents a new control approach to adapt the object tracking process to the scene condition variations. More Each context cluster is then associated to satisfactory tracking parameters. In the online control phase, once a context change is detected, the tracking parameters are tuned using the learned values. The approach has been experimented with three different tracking algorithms and on long, complex video datasets. This article brings two significant contributions: (1) a classification method of video sequences to learn offline tracking parameters, (2) a new method to tune online tracking parameters using tracking context.

Visual activity recognition plays a fundamental role in several research fields as a way to extract semantic meaning of images and videos. Prior work has mostly focused on classification tasks, where a label is given for a video clip. However, real life scenarios require a method to browse a continuous video flow, automatically identify relevant temporal segments and classify them accordingly to target activities. This paper proposes a knowledge-driven event recognition framework to address this problem. The novelty of the method lies in the combination of a constraint-based ontology language for event modeling with robust algorithms to detect, track and re-identify people using color-depth sensing (Kinect® sensor). This combination enables to model and recognize longer and more complex events and to incorporate domain knowledge and 3D information into the same models. Moreover, the ontology-driven approach enables human understanding of system decisions and facilitates knowledge transfer across different scenes. The proposed framework is evaluated with real-world recordings of seniors carrying out unscripted, daily activities at hospital observation rooms and nursing homes. Results demonstrated that the proposed framework outperforms state-of-the-art methods in a variety of activities and datasets, and it is robust to variable and low-frame rate recordings. Further work will investigate how to extend the proposed framework with uncertainty management techniques to handle strong occlusion and ambiguous semantics, and how to exploit it to further support medicine on the timely diagnosis of cognitive disorders, such as Alzheimer’s disease.

The complex scene conditions such as light change, high density of mobile objects or object occlusion can cause object mis-detections. When a tracker can not recover these mis-detections, the trajectory of an object is fragmented into some short trajectories called tracklets. As a result, tracking quality is reduced remarkably. In this paper, we propose a new approach to improve the tracking quality by a global tracker which merges all tracklets belonging to an object in the whole video. Particularly, we compute descriptor reliability over time based on their discrimination. On the other hand, a motion model is also combined with appearance descriptors in a flexible way to improve the tracking quality. The proposed approach is evaluated on four benchmark datasets. The obtained results show the robustness and effectiveness of our approach compared to tracking as well as tracklet linking approaches from state of the art.