Uncertainties in the sensor data such as measurement noise, false detections caused by clutter, as well as merged, split, incomplete or missed detections due to a sensor malfunction or occlusions (both due to the limited sensor field of view and objects in the scene) make multi-target tracking a very complicated task. Thus one of the big challenges is track management and correct data association between detections and tracks. In this contribution we present an algorithm for visual detection and tracking of multiple extended targets under occlusions and split and merge effects. Unlike most of the state-of-the-art approaches we utilize low-level information integrating it in a unified approach based on a thresholdfree probabilistic conception. The introduced scheme makes it possible to utilize information about composition of the measurements gained through tracking of dedicated feature points in the image and resolves data association ambiguities in a soft decision using a globally optimal probabilistic data association approach. Beside existence evolution consideration we also exploit the spatial and temporal relationship between stably tracked points and tracked objects, which along with observability analysis, allows us for reconstruction of compatible measurements and thus correct track update even in cases of splits, merges and partial occlusions of the tracked targets.
One of the big challenges in multi-target tracking is the track management and correct data association between measurements and tracks. Major reason for tracking errors are detection failures such as merged, split, incomplete or missed detections as well as clutter-based detections (phantom objects). Those effects combined with uncertainties in existence and number of objects in the scene as well as uncertainties in their observability and dynamic object state lead to gross tracking errors. In this contribution we present an algorithm for visual detection and tracking of multiple extended targets which is capable of coping with occlusions and split and merge effects. Unlike most of the state-of-the-art approaches we utilize information about the measurements' composition gained through tracking dedicated feature points in the image and in 3D space, which allows us to reconstruct the desired object characteristics from the data even in the case of detection errors due to above-mentioned reasons. The proposed Feature-Based Probabilistic Data Association approach resolves data association ambiguities in a soft threshold-free decision based not only on target state prediction but also on the existence and observability estimation modeled as two additional Markov chains. A novel measurement reconstruction scheme allows for a correct innovation in case of split, merged and incomplete measurements realizing thus a detection-by-tracking approach. This process is assisted by a grid based object representation which offers a lower abstraction level of targets extent and is used for detailed occlusion analysis
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