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In this paper, we aim to take mobile multi-object tracking to the next level. Current approaches work in a tracking-by-detection framework, which limits them to object categories for which pre-trained detector models are available.In contrast, we propose a novel tracking-before-detection approach that can track both known and unknown object categories in very challenging street scenes. Our approach relies on noisy stereo depth data in order to segment and track objects in 3D. At its core is a novel, compact 3D representation that allows us to robustly track a large variety of objects, while building up models of their 3D shape online. In addition to improving tracking performance, this representation allows us to detect anomalous shapes, such as carried items on a person's body. We evaluate our approach on several challenging video sequences of busy pedestrian zones and show that it outperforms state-of-the-art approaches.

In this paper we consider the problem of multi-person detection from the perspective of a head mounted stereo camera. As pedestrians close to the camera cannot be detected by classical full-body detectors due to strong occlusion, we propose a stereo depth-template based detection approach for close-range pedestrians. We perform a sliding window procedure, where we measure the similarity between a learned depth template and the depth image. To reduce the search space of the detector we slide the detector only over few selected regions of interest that are generated based on depth information. The region-of-interest selection allows us to further constrain the number of scales to be evaluated, significantly reducing the computational cost. We present experiments on stereo sequences recorded from a head-mounted camera setup in crowded shopping street scenarios and show that our proposed approach achieves superior performance on this very challenging data.

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This paper presents a robust real-time multi-person tracking framework for busy street scenes. Tracking-by-detection approaches have recently been successfully applied to this task. However, their run-time is still limited by the computationally expensive object detection component. In this paper, we therefore consider the problem of making best use of an object detector with a fixed and very small time budget. The question we ask is: given a fixed time budget that allows for detector-based verification of k small regions-of-interest (ROIs) in the image, what are the best regions to attend to in order to obtain stable tracking performance? We address this problem by applying a statistical Poisson process model in order to rate the urgency by which individual ROIs should be attended to. These ROIs are initially extracted from a 3D depth-based occupancy map of the scene and are then tracked over time. This allows us to balance the system resources in order to satisfy the twin goals of detecting newly appearing objects, while maintaining the quality of existing object trajectories.