ADT: Object Tracking Algorithm Based on Adaptive Detection

Abstract

Object tracking is one of the most fundamental and important fields in computer vision with a wide range of applications. Although great progress has been made in object tracking combined with detection, there is still enormous challenges in real-time applications and for the computer cannot effectively capture the temporal correlations of targets and background clutter. In order to improve the performance of tracking algorithms under complex unconstrained conditions, we propose a novel tracking framework based on adaptive detection, called adaptive detection tracking (ADT). First, we exploit the temporal correlation of the recurrent neural network to predict the target's motion direction and efficiently update the region of interest (RoI) in the narrow range of the next frame. Then, the algorithm utilizes the correlation filter to initialize the defined region of interest based on the threshold. If the Interaction of Union (IoU) of the predicted bounding box and the groundtruth bounding box is greater than the set threshold, the predicted bounding box will be directly output as the tracking results, whereas the detection is adaptively carried out in the determined RoI. Finally, the predicted bounding box refines the direction model as the input of the next frame to complete the whole tracking flow. Our proposed adaptive detection tracking mechanism can efficiently realize non-frame-by-frame adaptive detection with excellent tracking accuracy and is more robust in the unconstrained scenes, especially for occlusion. Comprehensive experiments demonstrate that our approach consistently achieves state-of-the-art results and runs in real-time on six large tracking benchmarks, including OTB100, VOT2016, VOT2017, TC128, UAV123 and LaSOT datasets. INDEX TERMS Recurrent neural network, adaptive detection, object tracking, correlation filtering, model compression. YUE MING received the B.S. degree in communication engineering, the M.Sc. degree in humancomputer interaction engineering, and the Ph.D. degree in signal and information processing from

Tracking of a Fixed-Shape Moving Object Based on the Gradient Descent Method

“…The algorithm has been compared with a target tracking algorithm based on Convolution Neural Network (TTACNN) [ 44 ], ADT: object tracking algorithm. Based on adaptive detection [ 45 ], vehicle tracking algorithm combining detector and tracker (VTACDT) [ 46 ], multi-object tracking for urban and multilane traffic (MTUMT) [ 47 ], adaptive weighted strategy and occlusion detection mechanism (AWSODM) [ 48 ] and approximate proximal gradient-based correlation filter (APGCF) [ 13 ]. Table 1 shows the execution times of these algorithms, while Table 2 represents the average tracking errors.…”

Cooperative Use of Recurrent Neural Network and Siamese Region Proposal Network for Robust Visual Tracking

“…To satisfy real-time requirements, Siamese network [10], which refers to correlation filtering [9], has attracted considerable research attention. Siamese network is a special neural network architecture consisting of two or more weight-sharing sub-networks.…”