‘Skimming-Perusal’ Tracking: A Framework for Real-Time and Robust Long-Term Tracking

@article{Yan2019SkimmingPerusalTA,
  title={‘Skimming-Perusal’ Tracking: A Framework for Real-Time and Robust Long-Term Tracking},
  author={B. Yan and Haojie Zhao and Dong Wang and Huchuan Lu and Xiaoyun Yang},
  journal={2019 IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2019},
  pages={2385-2393}
}
  • B. Yan, Haojie Zhao, Xiaoyun Yang
  • Published 4 September 2019
  • Computer Science
  • 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Compared with traditional short-term tracking, long-term tracking poses more challenges and is much closer to realistic applications. However, few works have been done and their performance have also been limited. In this work, we present a novel robust and real-time long-term tracking framework based on the proposed skimming and perusal modules. The perusal module consists of an effective bounding box regressor to generate a series of candidate proposals and a robust target verifier to infer… 
View on IEEE
View With Semantic Reader
81 Citations
Highly Influential Citations
12
Background Citations
34
Methods Citations
30
Results Citations
3

Figures and Tables from this paper

81 Citations

Citation Type

Citation Type

High-Performance Long-Term Tracking With Meta-Updater
TLDR
This work proposes a novel offline-trained Meta-Updater that can effectively integrate geometric, discriminative, and appearance cues in a sequential manner, and then mine the sequential information with a designed cascaded LSTM module.
GlobalTrack: A Simple and Strong Baseline for Long-term Tracking
TLDR
This work proposes GlobalTrack, a pure global instance search based tracker that makes no assumption on the temporal consistency of the target's positions and scales, and it is able to perform full-image and multi-scale search of arbitrary instances with only a single query as the guide.
CoCoLoT: Combining Complementary Trackers in Long-Term Visual Tracking
TLDR
CoLoT perceives whether the trackers are following the target object through an online learned deep verification model, and accordingly activates a decision policy which selects the best performing tracker as well as it corrects the performance of the failing one.
Exploring 3 R’s of Long-term Tracking: Re-detection, Recovery and Reliability
TLDR
This paper tests redetection capability of the trackers in the wild by simulating virtual cuts, investigates the role of chance in the recovery of tracker after failure and proposes a novel metric allowing visual inference on the ability of a tracker to track contiguously at a given accuracy.
Global Tracking via Ensemble of Local Trackers
TLDR
This work combines the advantages of both strategies: tracking the target in a global view while exploiting the temporal context; and performs global tracking via ensemble of local trackers spreading the full image.
VTT: Long-term Visual Tracking with Transformers
TLDR
This paper is the first to propose a novel architecture with transformers for long-term visual tracking that utilizes a transformer encoder-decoder architecture for aggregating global information to deal with occlusion and large scale or ratio variation.
Towards More Flexible and Accurate Object Tracking with Natural Language: Algorithms and Benchmark
  • Xiao Wang, Xiujun Shu, Feng Wu
  • Computer Science
    2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2021
TLDR
A new benchmark specifically dedicated to the tracking-by-language, including a large scale dataset, strong and diverse baseline methods, and a strong baseline method based on an adaptive local-global-search scheme is proposed for future works to compare.
Tracking by Joint Local and Global Search: A Target-aware Attention based Approach
TLDR
A novel and general target-aware attention mechanism (termed TANet) is proposed and integrated with a tracking-by-detection framework to conduct joint local and global search for robust tracking and resorts to adversarial training for better attention prediction.
Long-term target tracking combined with re-detection
TLDR
This work extends the state-of-the-art LADCF tracking algorithm with a re-detection component based on the SVM model, and introduces a robust confidence degree evaluation criterion that combines the maximum response criterion and the average peak-to correlation energy (APCE) to judge the confidence level of the predicted target.
Distractor-Aware Fast Tracking via Dynamic Convolutions and MOT Philosophy
TLDR
This work proposes a two-task tracking framework (named DMTrack), which utilizes two core components to achieve distractor-aware fast tracking via Dynamic convolutions (d-convs) and Multiple object tracking (MOT) philosophy, and constructs a lightweight one-shot detector using a novel dynamic convolutions generation method.
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 47 REFERENCES
Learning regression and verification networks for long-term visual tracking
TLDR
This work proposes a novel long-term tracking framework based on deep regression and verification networks, designed using the object-aware feature fusion and region proposal networks to generate a series of candidates and estimate their similarity scores effectively.
Now you see me: evaluating performance in long-term visual tracking
TLDR
An extensive evaluation of six long-term and nine short-term state-of-the-art trackers, using new performance measures, suitable for evaluatinglong-term tracking - tracking precision, recall and F-score shows that a good model update strategy and the capability of image-wide re-detection are critical for long- term tracking performance.
Long-term Tracking in the Wild: A Benchmark
TLDR
The OxUvA dataset and benchmark for evaluating single-object tracking algorithms is introduced, offering the community a large and diverse benchmark to enable the design and evaluation of tracking methods ready to be used “in the wild”.
Siamese Instance Search for Tracking
TLDR
It turns out that the learned matching function is so powerful that a simple tracker built upon it, coined Siamese INstance search Tracker, SINT, suffices to reach state-of-the-art performance.
The Sixth Visual Object Tracking VOT2018 Challenge Results
The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are…
Beyond Local Search: Tracking Objects Everywhere with Instance-Specific Proposals
TLDR
This work presents an object tracker that is not limited to a local search window and has ability to probe efficiently the entire frame and provides improved robustness for fast moving objects as well as for ultra lowframerate videos.
MUlti-Store Tracker (MUSTer): A cognitive psychology inspired approach to object tracking
TLDR
Inspired by the well-known Atkinson-Shiffrin Memory Model, this work proposes MUlti-Store Tracker (MUSTer), a dual-component approach consisting of short- and long-term memory stores to process target appearance memories.
Tracking-Learning-Detection
  • Zdenek Kalal
  • Computer Science
    IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2012
TLDR
A novel tracking framework (TLD) that explicitly decomposes the long-term tracking task into tracking, learning, and detection, and develops a novel learning method (P-N learning) which estimates the errors by a pair of “experts”: P-expert estimates missed detections, and N-ex Expert estimates false alarms.
ATOM: Accurate Tracking by Overlap Maximization
TLDR
This work proposes a novel tracking architecture, consisting of dedicated target estimation and classification components, and introduces a classification component that is trained online to guarantee high discriminative power in the presence of distractors.
Distractor-aware Siamese Networks for Visual Object Tracking
TLDR
This paper focuses on learning distractor-aware Siamese networks for accurate and long-term tracking, and extends the proposed approach for long- term tracking by introducing a simple yet effective local-to-global search region strategy.
...
1
2
3
4
5
...