An improved MEEM tracker via adaptive binary feature encoding

Abstract

We propose an adaptive binary feature encoding method to improve the tracking performance of the MEEM-tracker on real surveillance videos by enhancing the distinguished ability between the target object and the background. The adaptive binary feature encoding method transfers the source image data into binary features by calculating the online encoding parameters such as quantization number and quantization thresholds of each feature channel according to the current image data. The quantization number is calculated based on the dissimilarity between the target region and the surrounding region, and the quantization thresholds are decided by the feature clusters of each channel using the K-Means method. Our improved MEEM-tracker (IPMEEM) restores the online encoding parameters for producing distinguishing binary feature vectors in the current training and tracking procedure. In the experiments, our tracker achieves better overall performance on a surveillance dataset which has 12 new collected and labeled sequences under challenging scenes like “low contrast” and “low resolution”. We show that our tracker is more robust for real surveillance videos.