This paper proposes a part-based template matching method for multi-view human detection. The proposed method includes two stages: matching and verification. In particular, the best individual matching parts given a detection window are determined using an improved template matching algorithm. The hypothesis of the matched parts forming a human is then verified by employing a Bayesian-based model. The verification is not only based on the matching costs of individual parts but also how well the combining the matched parts satisfying the configuration constraints of the human body. Experimental results have shown that the proposed method is robust for detecting humans at multiple views and outperforms other template matching-based methods.