Abstract: Object detection is a hot topic in computer vision and it is the foundation of video caption.This paper proposed amulti-layer object detection algorithm based on multi-source feature late fusion,and used ways of multi-level decisions to divide the object detection task into two granularities.At the coarse level,the HOG feature was used to classify the images.According to the confidence scores of the classifier,the test images were categorized into positive,negative and uncertain examples.At the fine level,this paper proposed a multi-source feature late fusion method to classify the examples which are in the uncertain field.This paper conducted several comparative experiments on the same data set.Experimental results demonstrate that the proposed algorithm can obtain excellent results in all evaluation metrics,and achieve a better detection result than Faster-RCNN.

Key words: Computer vision, Feature extraction, Late fusion, Multi-level decision, Object detection