The Seventh Visual Object Tracking VOT2019 Challenge Results

Abstract

The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis as well as the standard VOT methodology for long-term tracking analysis. The VOT2019 challenge was composed of five challenges focusing on different tracking domains: (i) VOT-ST2019 challenge focused on short-term tracking in RGB, (ii) VOT-RT2019 challenge focused on "real-time" shortterm tracking in RGB, (iii) VOT-LT2019 focused on longterm tracking namely coping with target disappearance and reappearance. Two new challenges have been introduced: (iv) VOT-RGBT2019 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2019 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2019, VOT-RT2019 and VOT-LT2019 datasets were refreshed while new datasets were introduced for VOT-RGBT2019 and VOT-RGBD2019. The VOT toolkit has been updated to support both standard shortterm, long-term tracking and tracking with multi-channel imagery. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website 1 .

Fast Visual Object Tracking using Ellipse Fitting for Rotated Bounding Boxes

“…As well as, we basketball basketball fish1 graduate iceskater1 monkey polo surfing Figure 6. Qualitative results: We show some sample outputs on eight sequences selected from VOT2019 [20], where the red box is SiamMask [35], the cyan box is SiamRPN++ [24], the green box is SiamMask E(ours), and the blue box is the ground truth. need to beware the other dynamic distractors in the scene.…”

“…To address the visual object tracking problems, many benchmarks have been developed, such as Object Tracking Benchmark (OTB50 [36] and OTB100 [37]), and Visual Object Tracking Challenges (VOT2016 [21], VOT2018 [19], VOT2019 [20]). In OTB datasets, ground truth was labeled by axis aligned bounding boxes and while Init Output Figure 1.…”

Parallelization of the Honeybee Search Algorithm for Object Tracking

“…Two different recent trackers were selected for contrast: Struck [45] and SiamMask [10]. Struck remains relevant because it is a purely online tracking proposal that requires no kind of training and is still able to participate in the latest issues of the VOT challenge [64]. On the other hand, SiamMask is considered among the best tracker proposals that participated in the latest VOT challenge (top 20) in terms of accuracy, also several techniques that take inspiration from SiamMask are positioned in the toptier.…”

“…Since the ALOV dataset is mainly focused on accuracy. An interesting dataset could be the object tracking benchmark of Kristan et al [64]. Also, use state-of-the-art trackers and other well-known traditional trackers for testing.…”

An accelerated correlation filter tracker

“…Over the decades of development, numerous algorithms have been proposed for designing robust trackers with a certain degree of success [1,2,3]. As a byproduct, a variety of experimental protocols and evaluation metrics have been proposed to support academic research in terms of standard benchmarks [4] and technology pushing challenges [5,6,7,8]. However, there remain many challenging factors, such as background clutter, non-rigid deformation, abrupt motion, occlusion and real-time requirement, that impede accurate tracking performance in unconstrained scenarios.…”