Staple: Complementary Learners for Real-Time Tracking

Correlation Filter-based trackers have recently achieved excellent performance, showing great robustness to challenging situations exhibiting motion blur and illumination changes. However, since the model that they learn depends strongly on the spatial layout of the tracked object, they are notoriously sensitive to deformation. Models based on colour statistics have complementary traits: they cope well with variation in shape, but suffer when illumination is not consistent throughout a sequence. Moreover, colour distributions alone can be insufficiently discriminative. In this paper, we show that a simple tracker combining complementary cues in a ridge regression framework can operate faster than 80 FPS and outperform not only all entries in the popular VOT14 competition, but also recent and far more sophisticated trackers according to multiple benchmarks.Comment: To appear in CVPR 201

A Universal Update-pacing Framework For Visual Tracking

This paper proposes a novel framework to alleviate the model drift problem in visual tracking, which is based on paced updates and trajectory selection. Given a base tracker, an ensemble of trackers is generated, in which each tracker's update behavior will be paced and then traces the target object forward and backward to generate a pair of trajectories in an interval. Then, we implicitly perform self-examination based on trajectory pair of each tracker and select the most robust tracker. The proposed framework can effectively leverage temporal context of sequential frames and avoid to learn corrupted information. Extensive experiments on the standard benchmark suggest that the proposed framework achieves superior performance against state-of-the-art trackers.Comment: Submitted to ICIP 201

Online Object Tracking with Proposal Selection

Tracking-by-detection approaches are some of the most successful object trackers in recent years. Their success is largely determined by the detector model they learn initially and then update over time. However, under challenging conditions where an object can undergo transformations, e.g., severe rotation, these methods are found to be lacking. In this paper, we address this problem by formulating it as a proposal selection task and making two contributions. The first one is introducing novel proposals estimated from the geometric transformations undergone by the object, and building a rich candidate set for predicting the object location. The second one is devising a novel selection strategy using multiple cues, i.e., detection score and edgeness score computed from state-of-the-art object edges and motion boundaries. We extensively evaluate our approach on the visual object tracking 2014 challenge and online tracking benchmark datasets, and show the best performance.Comment: ICCV 201

Anticipatory synergy adjustments reflect individual performance of feedforward force control

We grasp and dexterously manipulate an object through multi-1 digit synergy. In the framework of the uncontrolled manifold (UCM) hypothesis, multi-digit synergy is defined as the coordinated control mechanism of fingers to stabilize variable important for task success, e.g., total force. Previous studies reported anticipatory synergy adjustments (ASAs) that correspond to a drop of the synergy index before a quick change of the total force. The present study compared ASA’s properties with individual performances of feedforward force control to investigate a relationship of those. Subjects performed a total finger force production task that consisted of a phase in which subjects tracked target line with visual information and a phase in which subjects produced total force pulse without visual information. We quantified their multi-digit synergy through UCM analysis and observed significant ASAs before producing total force pulse. The time of the ASA initiation and the magnitude of the drop of the synergy index were significantly correlated with the error of force pulse, but not with the tracking error. Almost all subjects showed a significant increase of the variance that affected the total force. Our study directly showed that ASA reflects the individual performance of feedforward force control independently of target-tracking performance and suggests that the multi-digit synergy was weakened to adjust the multi-digit movements based on a prediction error so as to reduce the future error

Understanding and Diagnosing Visual Tracking Systems

Several benchmark datasets for visual tracking research have been proposed in recent years. Despite their usefulness, whether they are sufficient for understanding and diagnosing the strengths and weaknesses of different trackers remains questionable. To address this issue, we propose a framework by breaking a tracker down into five constituent parts, namely, motion model, feature extractor, observation model, model updater, and ensemble post-processor. We then conduct ablative experiments on each component to study how it affects the overall result. Surprisingly, our findings are discrepant with some common beliefs in the visual tracking research community. We find that the feature extractor plays the most important role in a tracker. On the other hand, although the observation model is the focus of many studies, we find that it often brings no significant improvement. Moreover, the motion model and model updater contain many details that could affect the result. Also, the ensemble post-processor can improve the result substantially when the constituent trackers have high diversity. Based on our findings, we put together some very elementary building blocks to give a basic tracker which is competitive in performance to the state-of-the-art trackers. We believe our framework can provide a solid baseline when conducting controlled experiments for visual tracking research

Long-Term Visual Object Tracking Benchmark

We propose a new long video dataset (called Track Long and Prosper - TLP) and benchmark for single object tracking. The dataset consists of 50 HD videos from real world scenarios, encompassing a duration of over 400 minutes (676K frames), making it more than 20 folds larger in average duration per sequence and more than 8 folds larger in terms of total covered duration, as compared to existing generic datasets for visual tracking. The proposed dataset paves a way to suitably assess long term tracking performance and train better deep learning architectures (avoiding/reducing augmentation, which may not reflect real world behaviour). We benchmark the dataset on 17 state of the art trackers and rank them according to tracking accuracy and run time speeds. We further present thorough qualitative and quantitative evaluation highlighting the importance of long term aspect of tracking. Our most interesting observations are (a) existing short sequence benchmarks fail to bring out the inherent differences in tracking algorithms which widen up while tracking on long sequences and (b) the accuracy of trackers abruptly drops on challenging long sequences, suggesting the potential need of research efforts in the direction of long-term tracking.Comment: ACCV 2018 (Oral