RIHOG-BoVWs for Rotation-invariant Human Detection

[摘要] Rotation-invariant human detection is of vital significance to many applications, including victim detection via a daylight camera on an unmanned aerial vehicle (UAV). With this problem in mind, we propose a powerful rotation-invariant object-level description method, i.e., RIHOG-BoVWs, which is achieved by local-level feature calculation and high-level semantic feature extraction. In local-level feature calculation, we consider Rotation-invariant Histograms of Oriented Gradients (RIHOG) as the local descriptor and project gradient information into the frequency domain, where the rotation-invariant representation vector is constructed. In high-level semantic feature extraction, Bag of Visual Words (BoVWs) model is employed to achieve global description from local features without considering their spatial structures. Experimentally, we first confirm the sufficient discrimination power of the RIHOG-BoVWs on the public Freestyle Motocross dataset, and then demonstrate the high performance of RIHOG-BoVWs on a victim dataset which has varied backgrounds and body postures.

[发布机构] China Astronaut Research and Training Center, Beijing; 100094, China^1;Institute of Medical Support Technology, Academy of System Engineering, Academy of Military Sciences, Tianjin; 300161, China^2

[效力级别] 计算机科学

[关键词] Bag-of-visual words;Description method;Frequency domains;Gradient informations;High-level semantic features;Histograms of oriented gradients;Rotation invariant;Victim detections