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- W3103973397 abstract "Urban-oriented autonomous vehicles require a reliable perception technology to tackle the high amount of uncertainties. The recently introduced compact 3D LIDAR sensor offers a surround spatial information that can be exploited to enhance the vehicle perception. We present a real-time integrated framework of multi-target object detection and tracking using 3D LIDAR geared toward urban use. Our approach combines sensor occlusion-aware detection method with computationally efficient heuristics rule-based filtering and adaptive probabilistic tracking to handle uncertainties arising from sensing limitation of 3D LIDAR and complexity of the target object movement. The evaluation results using real-world pre-recorded 3D LIDAR data and comparison with state-of-the-art works shows that our framework is capable of achieving promising tracking performance in the urban situation." @default.
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- W3103973397 date "2018-01-01" @default.
- W3103973397 modified "2023-10-09" @default.
- W3103973397 title "Towards Multi-Object Detection and Tracking in Urban Scenario under Uncertainties" @default.
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- W3103973397 doi "https://doi.org/10.5220/0006706101560167" @default.
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