Cascade object detection with complementary features and algorithms

Abstract

This paper presents a novel method of combining the object detection algorithms and the methods used for image classification aiming to further boosting the object detection performance. Since the algorithm and image features which used in the image classification tasks have not been well transplanted into the object detection method, most of the reason is that the feature used in the image classification is extracted from the whole image which have no space information. In our framework, firstly we use the detection model to propose the candidate windows; in the second stage the candidate windows will act as the whole image to be classified. Intuitively, the first stage should have high recall, while the second stage should have high precision. In our proposed detection framework, a SVM model was trained to combine the scores computed from both stages. The proposed framework can be generally used, while in our experiments we used the LSVM as the object detector in the first stage and the mostly used deep convolutional neural network classifier in the second stage. Finally, a combined model shows that the object detection performance can be further boosted under this framework in our experiments.