Car detection at night using latent filters
Hossein Tehrani, Taiki Kawano, Seiichi Mita · 2014
Deformable part models (DPM) already proved great performance for objects detection and many extensions have already published in literature. DPMs generally have high performance, though they dramatically fail to detect objects in challenging environments such as night time. This paper proposes a method based on the idea of latent parts to optimize the structure of objects in deformable part models. Even in challenging environment some parts of the objects are still visible and latent parts can optimize the structure of DPM's object model to catch significant features. We have evaluated proposed method to detect cars at night time in urban area using IR camera. It is a challenging problem due to low visibility, light distortion and illumination/glare in urban area. Experimental results prove the effectiveness of the model to detect close and medium range cars in urban scenes at night time.