追跡と分類の同時実行による効率的で高精度な物体検出
Efficient and accurate object detection with simultaneous classification and tracking
点群ストリーム上で追跡器がデータ関連付けを行い検出器の冗長処理を回避するとともに、不確実な物体には複数視点の観測を融合するモデルを適用し、移動物体の検出精度と効率を向上させた研究。
著者: Xuesong Li, Jose Guivant
分類: cs.CV, cs.RO
原文アブストラクト
Interacting with the environment, such as object detection and tracking, is a crucial ability of mobile robots. Besides high accuracy, efficiency in terms of processing effort and energy consumption are also desirable. To satisfy both requirements, we propose a detection framework based on simultaneous classification and tracking in the point stream. In this framework, a tracker performs data association in sequences of the point cloud, guiding the detector to avoid redundant processing (i.e. classifying already-known objects). For objects whose classification is not sufficiently certain, a fusion model is designed to fuse selected key observations that provide different perspectives across the tracking span. Therefore, performance (accuracy and efficiency of detection) can be enhanced. This method is particularly suitable for detecting and tracking moving objects, a process that would require expensive computations if solved using conventional procedures. Experiments were conducted on the benchmark dataset, and the results showed that the proposed method outperforms original tracking-by-detection approaches in both efficiency and accuracy.
