To improve the performance of multi-object tracking in the complex scenario with frequent occlusions and cluttered backgrounds, a novel online multi-object tracking algorithm based on fuzzy logic is proposed. In the proposed algorithm, firstly, the similarity measure of multiple features between the objects and the measurements are calculated, including the background-weighted color feature, histogram of oriented gradients feature, local binary pattern feature and spatial distance feature. Secondly, the fuzzy rule base is constructed by incorporating the expert knowledge, which can adaptively allocate the weight of each feature by using fuzzy logic. The association probabilities between objects and measurements are substituted by the weighted sum of multiple features' similarity measure, which can effectively improve the accuracy of data association. Experimental results using challenging public datasets demonstrate that the improved performance of the proposed algorithm, compared with other state-of-the-art tracking algorithms.