Effective human detection via multi-model classification and adaptive late fusion

摘要

Human detection serves as an important basis to achieve certain video surveillance-oriented biometrics such as gait, face and actions since the first step is to find and locate human targets in surveillance scenes. In the literature, channel feature-based methods and deep neural network-based methods are two most popular kinds of human detection approaches, with their own advantages. However, there is not much effort on the study of their combination to take full advantage of these two kinds of approaches. Therefore in this paper, we propose an effective human detection approach by combining multiple state-of-the-art deep neural network-based and channel feature-based methods with an adaptive late fusion strategy. The key idea of our approach is to explore complementary information of different state-of-the-art detection methods and to find an appropriate way to combine their strong points for better performance. The proposed approach is evaluated on several standard human detection benchmarks, and shows its effectiveness by achieving superior performances to the other state-of-the-art methods on most evaluation settings.