延伸機器感知在有遮蔽時進行多目標追蹤：從單一感測器到異質性多感測器案例

Extended Machine Perception in Multi-Target Tracking with Occlusion: from Single Sensor to Heterogeneous Sensors

陳俊甫(Jiun-Fu Chen)

指導教授 ： 周承復

共同指導教授 ： 王傑智(Chieh-ChihWang)

國立臺灣大學/電機資訊學院/資訊工程學研究所/博士(2019年)

https://doi.org/10.6342/NTU201900949

摘要

多移動物體追蹤是許多智慧型應用系統所必需的關鍵基礎能力。為了要完成多移動物體追蹤的任務，非常需要來自感測器的重要觀測資訊。特別是在沒有感測資料時，像是有遮蔽發生時，幾乎不可能使用傳統的演算法來完成多移動物體追蹤；而且在有遮蔽的狀態下，移動物體會更難被成功追蹤到。在都會區交通情境中，遮蔽狀態會降低駕駛安全性；而在人體關節追蹤的應用情境，遮蔽狀態可能會造成估測錯誤，並且誤導對於復健運動成效的評估結果。因此本篇論文分別對兩個情境提出使用單顆二維光達 (LIDAR) 與使用異質性多感測器 (Heterogeneous Sensors) 的系統架構。第一個架構利用虛擬觀測模型 (Virtual Measurement Model) 與互動物體追蹤 (Interacting Object Tracking) 演算法來處理在擁擠都會區下遮蔽狀態所造成的影響；而第二個架構則是運用異質性感測器同時定位、追蹤、與建立模型 (Heterogeneous Sensor Simultaneous Localization, Tracking, and Modeling) 演算法來整合異質性感測器資訊，並且提供估測給中風復健動作評估使用。實驗結果顯示，在第一個應用中所提出的演算法可追蹤都會區路口中超過57%的被遮蔽移動物體；而第二個使用模擬生成與收集來自十位受測者資料的應用結果顯示出，提出的演算法可以得到無遮蔽時誤差4.6公分，有遮蔽時誤差18.1公分的成果。由此本篇論文成功展示了在都會區與室內環境下，解決遮蔽造成的問題與影響的能力。

關鍵字

多移動物體追蹤 ； 感測器融合 ； 遮蔽

並列摘要

Multi-Target tracking is a key ability for many intelligent systems in lots of applications. In order to accomplish the multi-target tracking, the measurements from the perceptive sensor plays a very important role. It is impossible to perform the multi-target tracking without sensory data especially such as occlusion situation, which increases the difficulty of the tracking task. Moreover, in the urban traffic situation, occlusion decreases the driving safety; and in the case of human joint tracking, occlusion may fails the estimates and leads to wrong judgement for evaluating the performance of rehabilitation activities. Here, two frameworks are presented and described for a stationary 2D LIDAR and for heterogeneous sensors. The first framework introduces the virtual measurement model with interacting object tracking scheme to tackle the effects of the occlusion in crowded urban environments. The second framework applies the heterogeneous sensor simultaneous localization, tracking, and modeling algorithm to fuse heterogeneous sensors and to provide estimates within occlusion for motion evaluation in stroke rehabilitation process. The ample experimental results of the first application show that the interact object tracking scheme tracks over 57% of occluded moving object for the daunting task in an urban intersection. While the results of the second application with synthetic data and collected from ten subjects reveal that the proposed approach yields 4.6 cm error in observed cases and 18.1 cm error during burst occlusion. We successfully demonstrate the capability to resolve issues and effects in occlusion for both urban and indoor environments.

並列關鍵字

Multi-Target Tracking ； Sensor Fusion ； Occlusion

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