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We present a method to perform online Multiple Object Tracking (MOT) of known object categories in monocular video data. Current Tracking-by-Detection MOT approaches build on top of 2D bounding box detections. In contrast, we exploit state-of-the-art instance aware semantic segmentation techniques to compute 2D shape representations of target objects in each frame. We predict position and shape of segmented instances in subsequent frames by exploiting optical flow cues. We define an affinity matrix between instances of subsequent frames which reflects locality and visual similarity. The instance association is solved by applying the Hungarian method. We evaluate different configurations of our algorithm using the MOT 2D 2015 train dataset. The evaluation shows that our tracking approach is able to track objects with high relative motions. In addition, we provide results of our approach on the MOT 2D 2015 test set for comparison with previous works. We achieve a MOTA score of 32.1.

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This work proposes a novel approach to reconstruct three-dimensional vehicle trajectories in monocular video sequences. We leverage state-of-the-art instance-aware semantic segmentation and optical flow methods to compute object video tracks on pixel level. This approach uses Structure from Motion to determine camera poses relative to vehicle instances and environment structures. We parameterize vehicle trajectories with a single variable by combining object and background reconstructions. The naive combination of vehicle and environment reconstruction results in inconsistent motion trajectories due to the scale ambiguity of SfM. We determine consistent object trajectories by projecting dense vehicle reconstructions on the terrain surface. Our scale ratio estimation approach shows no degenerated camera-vehicle-motions. We demonstrate the usefulness of our approach using publicly available video data of driving scenarios. We extend this evaluation showing trajectory reconstruction results using drone footage. We use synthetic data of vehicles in urban environments to evaluate the proposed algorithm. We achieve an average reconstructionto-ground-truth distance of 0.17 meter.

Single visual object tracking from an unmanned aerial vehicle (UAV) poses fundamental challenges such as object occlusion, small-scale objects, background clutter, and abrupt camera motion. To tackle these difficulties, we propose to integrate the 3D structure of the observed scene into a detection-by-tracking algorithm. We introduce a pipeline that combines a model-free visual object tracker, a sparse 3D reconstruction, and a state estimator. The 3D reconstruction of the scene is computed with an image-based Structure-from-Motion (SfM) component that enables us to leverage a state estimator in the corresponding 3D scene during tracking. By representing the position of the target in 3D space rather than in image space, we stabilize the tracking during ego-motion and improve the handling of occlusions, background clutter, and small-scale objects. We evaluated our approach on prototypical image sequences, captured from a UAV with low-altitude oblique views. For this purpose, we adapted an existing dataset for visual object tracking and reconstructed the observed scene in 3D. The experimental results demonstrate that the proposed approach outperforms methods using plain visual cues as well as approaches leveraging image-space-based state estimations. We believe that our approach can be beneficial for trafficmonitoring, video surveillance, and navigation.