Visual Tracking: An Experimental Survey Smeulders, Arnold W. M.; Chu, Dung M.; Cucchiara, Rita ...
IEEE transactions on pattern analysis and machine intelligence,
07/2014, Letnik:
36, Številka:
7
Journal Article
Recenzirano
Odprti dostop
There is a large variety of trackers, which have been proposed in the literature during the last two decades with some mixed success. Object tracking in realistic scenarios is a difficult problem, ...therefore, it remains a most active area of research in computer vision. A good tracker should perform well in a large number of videos involving illumination changes, occlusion, clutter, camera motion, low contrast, specularities, and at least six more aspects. However, the performance of proposed trackers have been evaluated typically on less than ten videos, or on the special purpose datasets. In this paper, we aim to evaluate trackers systematically and experimentally on 315 video fragments covering above aspects. We selected a set of nineteen trackers to include a wide variety of algorithms often cited in literature, supplemented with trackers appearing in 2010 and 2011 for which the code was publicly available. We demonstrate that trackers can be evaluated objectively by survival curves, Kaplan Meier statistics, and Grubs testing. We find that in the evaluation practice the F-score is as effective as the object tracking accuracy (OTA) score. The analysis under a large variety of circumstances provides objective insight into the strengths and weaknesses of trackers.
Gold codes are widely used for pseudo-noise (PN) ranging, and non-commensurate sampling is an effective solution to improving the tracking performance of digital code tracking loops. However, the ...sequence transition density of gold codes is low, which limits the achievable ranging accuracy. Here, the authors present a non-commensurate sampling and double-loop-based regenerative PN code tracking approach. With this approach, the tracking of the original regenerative code is equivalent to that of its clock component, and the sequence transition density is maximised. Thus, the tracking error is apparently reduced and optimal tracking performance is achieved. In the double-loop structure, a unified tracking loop is employed to facilitate performance comparison. Simulation results demonstrate the performance improvement of the proposed approach over the traditional gold code tracking scheme, and the relationship between the non-commensurate sampling parameters and the achievable tracking accuracy. The approach is especially suitable for precision ranging applications, and can be extended to other applications involving accurate signal tracking in digital equipment.
In this article, for a marine aerial-surface heterogeneous (MASH) system composed by a quadrotor unmanned aerial vehicle (UAV) and an unmanned surface vehicle (USV) with heterogeneity, completely ...unknown dynamics and disturbances, the accurate trajectory-tracking problem is solved by creating a novel coordinated trajectory-tracking control (CTTC) scheme. A family of coordinate transformations are devised to convert the MASH system tracking error dynamics into translation-rotation cascade manners, whereby the heterogeneity is removed and finite-time observers for complex unknowns are facilitated. In conjunction with sliding mode based rotation error dynamics, distributed tracking controllers for the quadrotor UAV and the USV are independently synthesized such that cascade tracking error dynamics are globally asymptotically stable. With the aid of cascade and Lyapunov analysis, the entire CTTC solution to the accurate trajectory-tracking problem of the MASH system is eventually put forward. Simulation results and comprehensive comparisons on a prototype MASH system demonstrate the effectiveness and superiority of the proposed CTTC scheme.
Recently, most multiple object tracking (MOT) algorithms adopt the idea of tracking-by-detection. Relevant research shows that the performance of the detector obviously affects the tracker, while the ...improvement of detector is gradually slowing down in recent years. Therefore, trackers using tracklet (short trajectory) are proposed to generate more complete trajectories. Although there are various tracklet generation algorithms, the fragmentation problem still often occurs in crowded scenes. In this paper, we introduce an iterative clustering method that generates more tracklets while maintaining high confidence. Our method shows robust performance on avoiding internal identity switch. Then we propose a deep association method for tracklet association. In terms of motion and appearance, we construct motion evaluation network (MEN) and appearance evaluation network (AEN) to learn long-term features of tracklets for association. In order to explore more robust features of tracklets, a tracklet-based training mechanism is also introduced. Tracklet groups are used as the input of the networks instead of discrete detections. Experimental results show that our training method enhances the performance of the networks. In addition, our tracking framework generates more complete trajectories while maintaining the unique identity of each target as the same time. On the latest MOT 2017 benchmark, we achieve state-of-the-art results.
In this study, we propose a novel RGB-T tracking framework by jointly modeling both appearance and motion cues. First, to obtain a robust appearance model, we develop a novel late fusion method to ...infer the fusion weight maps of both RGB and thermal (T) modalities. The fusion weights are determined by using offline-trained global and local multimodal fusion networks, and then adopted to linearly combine the response maps of RGB and T modalities. Second, when the appearance cue is unreliable, we comprehensively take motion cues, i.e., target and camera motions, into account to make the tracker robust. We further propose a tracker switcher to switch the appearance and motion trackers flexibly. Numerous results on three recent RGB-T tracking datasets show that the proposed tracker performs significantly better than other state-of-the-art algorithms.
Tracking often uses a multistage pipeline of feature extraction, target information integration, and bounding box estimation. To simplify this pipeline and unify the process of feature extraction and ...target information integration, we present a compact tracking framework, termed as MixFormer, built upon transformers. Our core design is to utilize the flexibility of attention operations, and propose a Mixed Attention Module (MAM) for simultaneous feature extraction and target information integration. This synchronous modeling scheme allows to extract target-specific discriminative features and perform extensive communication between target and search area. Based on MAM, we build our MixFormer tracking framework simply by stacking multiple MAMs with progressive patch embedding and placing a localization head on top. In addition, to handle multiple target templates during online tracking, we devise an asymmetric attention scheme in MAM to reduce computational cost, and propose an effective score prediction module to select high-quality templates. Our MixFormer sets a new state-of-the-art performance on five tracking benchmarks, including LaSOT, TrackingNet, VOT2020, GOT-10k, and UAV123. In particular, our MixFormer-L achieves NP score of 79.9% on LaSOT, 88.9% on TrackingNet and EAO of 0.555 on VOT2020. We also perform in-depth ablation studies to demonstrate the effectiveness of simultaneous feature extraction and information integration. Code and trained models are publicly available at https://github.com/MCG-NJU/MixFormer.
•Proposal of a new template update strategy for real-time tracking in industrial monitoring.•Our method achieves significant accuracy under background clutter.•A novel multi-template update tracker ...based on the update strategy is proposed.•A thorough performance comparison with other methods, proving the robustness of our method.•Our strategy can play an important role in the safety of the automation industry.
Reliability has been widely used in industrial IoT (IIoT) applications. Since maintaining fast and accurate tracking of targets with fast move and motion blur in industrial applications is still a major challenge, this paper proposes a novel mechanism based on reliability for target matching, which is the basic problem in computer vision. Then, by using the proposed reliability-based mechanism, a novel visual tracking method with edge computing is proposed to achieve accurate and rapid tracking with high reliability. Experimental results on Object Tracking Benchmark (OTB) dataset showed effectiveness of the proposed mechanism by comparing reliability-based and original algorithm. Results also showed that tracking performance of the proposed method has been increased, especially effected greatly on fast-moving, background clutter and motion blur. Therefore, the proposed method is validated to play an important role in real industrial applications with edge computing, which is more suitable for IIoT environments and automotive industry.
This paper proposes a novel iterative maximum weighted independent set (MWIS) algorithm for multiple hypothesis tracking (MHT) in a tracking-by-detection framework. MHT converts the tracking problem ...into a series of MWIS problems across the tracking time. Previous works solve these NP-hard MWIS problems independently without the use of any prior information from each frame, and they ignore the relevance between adjacent frames. In this paper, we iteratively solve the MWIS problems by using the MWIS solution from the previous frame rather than solving the problem from scratch each time. First, we define five hypothesis categories and a hypothesis transfer model, which explicitly describes the hypothesis relationship between adjacent frames. We also propose a polynomial-time approximation algorithm for the MWIS problem in MHT. In addition to that, we present a confident short tracklet generation method and incorporate tracklet-level association into MHT, which further improves the computational efficiency. Our experiments on both MOT16 and MOT17 benchmarks show that our tracker outperforms all the previously published tracking algorithms on both MOT16 and MOT17 benchmarks. Finally, we demonstrate that the polynomial-time approximate tracker reaches nearly the same tracking performance.
Multi-animal tracking (MAT), a multi-object tracking (MOT) problem, is crucial for animal motion and behavior analysis and has many crucial applications such as biology, ecology and animal ...conservation. Despite its importance, MAT is largely under-explored compared to other MOT problems such as multi-human tracking due to the scarcity of dedicated benchmarks. To address this problem, we introduce
AnimalTrack
, a dedicated benchmark for multi-animal tracking in the wild. Specifically, AnimalTrack consists of 58 sequences from a diverse selection of 10 common animal categories. On average, each sequence comprises of 33 target objects for tracking. In order to ensure high quality, every frame in AnimalTrack is manually labeled with careful inspection and refinement. To our best knowledge, AnimalTrack is the
first
benchmark dedicated to multi-animal tracking. In addition, to understand how existing MOT algorithms perform on AnimalTrack and provide baselines for future comparison, we extensively evaluate 14 state-of-the-art representative trackers. The evaluation results demonstrate that, not surprisingly, most of these trackers become degenerated due to the differences between pedestrians and animals in various aspects (e.g., pose, motion, and appearance), and more efforts are desired to improve multi-animal tracking. We hope that AnimalTrack together with evaluation and analysis will foster further progress on multi-animal tracking. The dataset and evaluation as well as our analysis will be made available upon the acceptance.
This study proposes an adaptive non-singular integral terminal sliding mode control (ANITSMC) scheme for trajectory tracking of autonomous underwater vehicles (AUVs) with dynamic uncertainties and ...time-varying external disturbances. The ANITSMC is first proposed for a first-order uncertain non-linear dynamic system to eliminate the singularity problem in conventional terminal sliding mode control (TSMC) and avoid the requirement of the bound information of the lumped system uncertainty. The time taken to reach the equilibrium point from any initial error is guaranteed to be finite. The proposed ANITSMC is then applied to trajectory tracking control of AUVs. It guarantees that the velocity tracking errors locally converge to zero in finite time and after that the position tracking errors locally converge to zero exponentially. The designed ANITSMC of AUVs avoids the requirement of the prior knowledge of the lumped system uncertainty bounds as opposite to the existing globally finite-time stable tracking control (GFTSTC), provides higher tracking accuracy than the existing GFTSTC and adaptive non-singular TSMC (ANTSMC) and offers faster convergence rate and better robustness against dynamic uncertainties and time-varying external disturbances than the adaptive proportional-integral sliding mode control (APISMC). Comparative simulation results are presented to validate the superiority of the ANITSMC over the APISMC.
PDF
