Abstract
High tracking frame rates have been achieved based on traditional tracking methods which however would fail due to drifts of the object template or model, especially when the object disappears from the camera’s field of view. To deal with it, tracking-and-detection-combination has become more and more popular for long-term unknown object tracking, whose detector almost does not drift and can regain the disappeared object when it comes back. However, for online machine learning and multiscale object detection, expensive computing resources and time are required. So it is not a good idea to combine tracking and detection sequentially like Tracking-Learning-Detection algorithm. Inspired from parallel tracking and mapping, this article proposes a framework of parallel tracking and detection for unknown object tracking. The object tracking algorithm is split into two separate tasks—tracking and detection which can be processed in two different threads, respectively. One thread is used to deal with the tracking between consecutive frames with a high processing speed. The other thread runs online learning algorithms to construct a discriminative model for object detection. Using our proposed framework, high tracking frame rates and the ability of correcting and recovering the failed tracker can be combined effectively. Furthermore, our framework provides open interfaces to integrate state-of-the-art object tracking and detection algorithms. We carry out an evaluation of several popular tracking and detection algorithms using the proposed framework. The experimental results show that different tracking and detection algorithms can be integrated and compared effectively by our proposed framework, and robust and fast long-term object tracking can be realized.
Highlights
As one of the major research topics in computer vision, object tracking is defined as estimating the location of a tracked object in continuous frames
We propose a parallel tracking and detection (PTAD) framework which contains two parallel modules: a tracker as a front end is performed in real time with low computation costs; a detector including online learning and object detection regarding as a back end needs more computing resources and can be processed at a lower speed
We test the performance of the selected trackers (KCF, kernelized correlation filter (KCF) with tracking failure detection based on the normalized correlation coefficient (NCC) method and median flow (MF)) on the TLD data set
Summary
As one of the major research topics in computer vision, object tracking is defined as estimating the location of a tracked object in continuous frames. In the Tracking-Learning-Detection (TLD) algorithm, the tracking and detection are performed simultaneously to realize long-term object tracking.[1] due to the fact that the tracking and detection tasks are placed in the same thread and run sequentially at the same frame rates, it presents several limitations in practical applications. The consumption of computing resources must be limited to the right range so as to guarantee the real-time performance; many popular and successful tracking, detection, and learning algorithms in the pattern recognition community with high computation cost cannot be integrated into the TLD framework. It is unnecessary to perform both multiscale object detection and object model updating for all frames, from which a detector can be run at a lower frame rate than that of a tracker
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