Abstract: Those existing delayed association algorithms of non-overlapping cameras always lead to target mismatching and subsequence match missing. To solve these two problems, this paper proposes pedestrian instant alignment through discriminative feature learning. First, this paper employs a siamese network to integrates a pedestrian verification model with a pedestrian identification one. The integrated model can extract discriminative appearance features from single frame of targeted pedestrian and calculate similarity for a pair of pedestrians.Second, this paper presents an instant alignment model for pedestrians across non-overlapping cameras. The fundamental of proposed instant alignment model is minimum cost flow algorithm. Hence, according to a match degree which is associated with appearance, spatial and temporal context, a dynamic minimum cost flow graph is established and solved in real time. The experimental results show that, on the pedestrian recognition datasets Market-1501 and CUHK03, the combination of pedestrian verification and identification model can improve the efficiency of feature extraction and the generalization ability significantly. The alignment performance of the proposed model is superior to the Gate-SCNN and S-LSTM. Furthermore, on dataset NLPR_MCT, the benchmark dataset for pedestrian tracking of non-overlapping cameras, the instant alignment accuracy of the proposed model increases by 3.3% compared to the champion algorithm in 2014 ECCV cross-camera pedestrian tracking challenge, which is a delayed association algorithm. The experiment results also show that the proposed model ranks second, just 6.6% lower than the state-of-the-art performance. When the propose model is applied to inter-cameras pedestrian tracking, the tracking accuracy is also higher than most popular algorithms.