Bird Detection Combined with the DPM Model Aggregate Channel Features in Natural Scene

Deformable Part Model (DPM) has achieved good results in target detection, but because of lacking bird sample and unbalanced training set, and single HOG feature cannot characterize bird target exactly, so the algorithm cannot meet high bird detecting accuracy in nature scenes. According to these problems, firstly, this paper filter and analyze the bird samples from ImageNet dataset, then choose 1 500 bird samples from natural scenes, generating the corresponding annotation files, then set up a dedicated bird data set. A new kind of bird detection in nature scene method which combines with DPM and Aggregate Channel Features is proposed. The algorithm extracts ACF (Aggregate Channel Features,ACF) from training samples of the dedicated bird dataset, and gets ACF-DPM model through LatentSVM training. The effects of component and part numbers on bird detection are further researched. Experimental results show that even in a complex nature scene, bird detection can be done effectively with this algorithm, the overall accuracy of which is better than traditional DPM algorithm.