We propose a deep radar object detection network (RODNet), to effectively detect objects purely from the carefully processed radar frequency data in the format of range-azimuth frequency heatmaps (RAMaps). Instead of using burdensome human-labeled ground truth, we train the RODNet using the annotations generated automatically by a novel 3D localization method using a camera-radar fusion (CRF) strategy. After intensive experiments, our RODNet shows favorable object detection performance without the presence of the camera.
Yizhou Wang, Yen-Ting Huang, Jeng-Neng Hwang.
3D localization of objects in road scenes is important for autonomous driving and advanced driver-assistance systems (ADAS). However, with common monocular camera setups, 3D information is difficult to obtain. In this paper, we propose a novel and robust method for 3D localization of monocular visual objects in road scenes by joint integration of depth estimation, ground plane estimation, and multi-object tracking techniques.
Yizhou Wang, Liangliang Cao.
We proposed a novel super-resolution approach for GIFs, which uses two high-resolution frames (the first and last frames) as well as the low-resolution data to generate a high-resolution GIF. To validate this approach, we collect a new super-resolution dataset for GIFs. The experiments on this dataset show that the performance of our algorithm significantly outperforms the popular video super-resolution baselines while achieving at least 80 times speedup on CPU.
Yizhou Wang, Zheng Shou, Shih-Fu Chang.
We proposed a series of new web-based methods of demonstration for TAL problem, including snippet-level and frame-level demonstration. On the demo website, users can either upload video or select video from THUMOS'14 to processing TAL algorithms. TAL algorithm available: Segment-CNN and CDC Networks. The demonstration methods we proposed can give users TAL results clearly and effciently.