Abstract
Due to the deficiency of prior information for online updating process, the tracking accuracy of fully-convolutional Siamese network (SiamFC) in complex scenes such as similar object interference, fast moving, and appearance change is not good. To solve the problem, a new object tracker based on a dynamic template updating strategy and the re-location mechanism based on the adaptive Kalman is proposed. To suppress the object interference and overcome the instability of fast-moving object tracking, an adaptive Kalman filter method is designed to change the selection method of search region and select the bounding box of the object closest to the predicted position. For the adaptation of appearance change, the high-confidence tracking results are fused with the initial template to dynamic update the template. Compared with traditional Kalman filter, the expectation of residual error for the adaptive Kalman filter method can be controlled in a low range by the adjustment of the gain online. The introduction of the adaptive Kalman based re-location mechanism improves the discriminative ability of SiamFC in interference scene. With the dynamic template updating strategy, the tracker obtains strong generalization capability to adapt to the appearance change of the tracking target. It is demonstrated that the proposed method performs real-time object tracking at the speed of 43fps and achieves competitive performance on OTB, VOT and TC128 datasets compared with other state-of-the-art trackers.
Highlights
Object tracking is a fundamental task in computer vision, such as autonomous driving, video surveillance, human motion analysis [1]–[5]
The main difficulty of object tracking is to build an object tracker, which can adapt to various complex scenes such as appearance changes, fast motion, and similar object interfering
The tracking algorithms based on convolutional neural networks (CNNs) have demonstrated their superior accuracy over traditional tracking algorithms, such as DeepSRDCF [11] and effcient convolution operator (ECO) [12]
Summary
Object tracking is a fundamental task in computer vision, such as autonomous driving, video surveillance, human motion analysis [1]–[5]. 2) A re-location mechanism based on the adaptive Kalman filter is proposed to improve the discriminative ability of SiamFC in interference scenes. TRACKERS BASED ON DEEP LEARNING In recent years, many researchers combined deep features with traditional correlation filter methods for the object tracking problem. Zhou et al [21] combined the Kalman filter with SiamFC to process complex tracking scenes and change the selection method of the search region for stably tracking fast moving targets. A re-location mechanism based on the adaptive Kalman filter is proposed to suppress the object interference and overcome the instability of fast-moving object tracking. To realize real-time tracking, the feature extractor is usually used based on a convolutional neutral network with simple structure like AlexNet [29]
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