An Empirical Study of Visual Features for Part Based Model
Object detection is a fundamental task in computer vision. Deformable part based model has achieved great success in the past several years, demonstrating very promising performance. Many papers emerge on part based model such as structure learning, learning more discriminative features. To help researchers better understand the existing visual features’ potential for part based object detection and promote the deep research into part based object representation, we propose an evaluation framework to compare various visual features’ performance for part based model. The evaluation is conducted on challenging PASCAL VOC2007 dataset which is widely recognized as a benchmark database. We adopt Average Precision (AP) score to measure each detector’s performance. Finally, the full evaluation results are present and discussed.
Junge Zhang Yinan Yu Shuai Zheng Kaiqi Hua
National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of Sciences National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of Sciences
国际会议
北京
英文
219-223
2011-11-28（万方平台首次上网日期，不代表论文的发表时间）