A tracking-by-detection multi-target algorithm is proposed based on feature fusion and discriminative appearance models. To identify the responses of the detector in each sliding window

a dual-threshold strategy is adopted to perform low-level association and generate reliable tracklets between adjacent frames. Training samples are collected from these tracklets. Then

we merge several features to robustly describe the training samples and use the Adaboost algorithm to train the classifier

i.e.

discriminative appearance model

online. Finally

the discriminative appearance model is used to link the tracklets into longer ones to form the final complete target trajectories by an iterative process. Experimental results on four challenging databases(TUD-Stadtmitte

TUD-Campus

TUD-Crossing

and Town-Center) show that the proposed method can efficiently deal with occlusions

target deformation

and background interference. The tracking results on the TUD-Crossing database are quantitatively analyzed

and the performance metrics of our algorithm are as follows: the FAF is 0.21

the MT is 84.6%

the ML is 7.7%

the Frag is 9

and the IDS is 4. The proposed method outperforms several state-of-the-art approaches in terms of FAF and Frag. The multi-feature fusion is appropriate for target expression

and the discriminative appearance model is effective for tracklets association. The proposed algorithm exhibits satisfactory performance in a complex scene and can be further applied to the preprocessing of some advanced algorithms

such as trajectory retrieval in behavior recognition.