2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDARS-1.2
Paper Title Enhancing Multi-step Action Prediction for Active Object Detection
Authors Fen Fang, Qianli Xu, Nicolas Gauthier, Liyuan Li, Joo-Hwee Lim, Institute for Infocomm Research, Singapore
SessionARS-1: Object Detection
LocationArea I
Session Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Poster
Topic Image and Video Analysis, Synthesis, and Retrieval: Image & Video Interpretation and Understanding
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract View planning, i.e., predicting next best views, is a key technical component of active object detection. It has been shown that adopting multi-step action in a reinforcement learning setup can boost the efficiency and accuracy of active object detection. However, existing methods suffer from unstable detection outcome when one adopts a naive strategy to combine the Q-values of multiple branches of action advantages, namely, action range and action type. This is partially caused by the lack of independence of these branches. To tackle this issue, we propose a novel mechanism to disentangle action range from action type. We evaluate our method on two public dataset and compare it with competitive benchmarks. It is shown that our method facilitates substantial gain in view planning efficiency, while enhancing detection accuracy.