A Color Histogram Based Large Motion Trend Fusion Algorithm for Vehicle Tracking

Due to the static nature of roadside cameras, targets of images have the characteristics of near big and far small. In a short time, as distances between targets and cameras get more faraway and the pixel ratios of the same target decrease sharply, which leads to a decrease in appearance information. However, the loss of appearance information makes tracking more challenging in vehicle tracking. To solve this problem, we propose a color histogram based large motion trend fusion (LMT-CH) algorithm for vehicle tracking, suitable for the traffic scene taken by the roadside camera. Based on Generalized Intersection-over-Union and Hungarian algorithm, LMT-CH uses targets of the large motion trend combined with the color histogram to modify the associated results. The algorithm includes four key steps: 1) train model based on the EfficientDet framework as an object detector. 2) First, calculate the correlation matrix between two consecutive frames. Second, get the matching results based on the Hungarian algorithm. Then, divide targets into four different sets based on the matching results. 3) Calculate the color histogram and large motion trend of the target in the suspected increase set of current frame's targets. 4) Re-identification trajectories based on large motion trend and color histogram. We evaluate the algorithm for the UA-DETRAC datasets with different levels of congestion. In several experiments, we show that the algorithm can effectively deal with the problem of target occlusion and loss, and it performs well in the ID switch of long-distance targets.