Object Tracking via Dual Linear Structured SVM and Explicit Feature Map

Ning, Jifeng; Yang, Jimei; Jiang, Shaojie; Zhang, Lei; Yang, Ming–Hsuan

doi:10.1109/cvpr.2016.462

Cited by 231 publications

(144 citation statements)

References 25 publications

Supporting

Mentioning

143

Contrasting

Order By: Relevance

“…The trackers of , Qi et al (2016); Nam and Han (2016), Bertinetto et al (2016b) are indicative trackers that employ neural networks and achieve top results. STRUCK (Hare et al 2011) is a discriminative tracker that performed very well in the Online Object Tracking benchmark , while the more recent method of Ning et al (2016) improves the computational burden of the structural SVM of STRUCK and reports superior results. SPOT (Zhang and van der Maaten 2014) is a strong performing part based tracker, CMT (Nebehay and Pflugfelder 2015) is a strong performing keypoint based tracker, LRST (Zhang et al 2014d) and ORIA (Wu et al 2012) are recent generative trackers.…”

Section: Model Free Trackingmentioning

confidence: 99%

A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

et al. 2017

View full text Add to dashboard Cite

Recently, technologies such as face detection, facial landmark localisation and face recognition and verification have matured enough to provide effective and efficient solutions for imagery captured under arbitrary conditions (referred to as "in-the-wild"). This is partially attributed to the fact that comprehensive "in-the-wild" benchmarks have been developed for face detection, landmark localisation and recognition/verification. A very important technology that has not been thoroughly evaluated yet is deformable face tracking "in-the-wild". Until now, the performance has mainly been assessed qualitatively by visually assessing the result of a deformable face tracking technology on short videos. In this paper, we perform the first, to the best of our knowledge, thorough evaluation of state-of-theart deformable face tracking pipelines using the recently introduced 300 VW benchmark. We evaluate many different architectures focusing mainly on the task of on-line deformable face tracking. In particular, we compare the following general strategies: (a) generic face detection plus generic facial landmark localisation, (b) generic model free tracking plus generic facial landmark localisation, as well as (c) hybrid approaches using state-of-the-art face detection, model free tracking and facial landmark localisation technologies. Our evaluation reveals future avenues for further research on the topic.

show abstract

Section: Model Free Trackingmentioning

confidence: 99%

A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

et al. 2017

View full text Add to dashboard Cite

show abstract

“…We compare SATIN with nine state-of-the-art trackers, including ACFN [60], CREST [61], DSST [11], SiamTri [62], SiamRPN [25], SiamFC [21], DeepLMCF [63], DLSSVM [64] and KCF [10], on the OTB benchmark datasets [43,44]. Among the participants, we treat SiamFC and SiamRPN as our baseline.…”

Section: Experiments On Otbmentioning

confidence: 99%

Siamese attentional keypoint network for high performance visual tracking

Gao

Yuan

Wang

et al. 2020

Knowledge-Based Systems

View full text Add to dashboard Cite

Visual tracking is one of the most fundamental topics in computer vision. Numerous tracking approaches based on discriminative correlation filters or Siamese convolutional networks have attained remarkable performance over the past decade. However, it is still commonly recognized as an open research problem to develop robust and effective trackers which can achieve satisfying performance with high computational and memory storage efficiency in real-world scenarios. In this paper, we investigate the impacts of three main aspects of visual tracking, i.e., the backbone network, the attentional mechanism, and the detection component, and propose a Siamese Attentional Keypoint Network, dubbed SATIN, for efficient tracking and accurate localization. Firstly, a new Siamese lightweight hourglass network is specially designed for visual tracking. It takes advantage of the benefits of the repeated bottom-up and top-down inference to capture more global and local contextual information at multiple scales. Secondly, a novel cross-attentional module is utilized to leverage both channel-wise and spatial intermediate attentional information, which can enhance both discriminative and localization capabilities of feature maps. Thirdly, a keypoints detection approach is invented to trace any target object by detecting the top-left corner point, the centroid point, and the bottom-right corner point of its bounding box. Therefore, our SATIN tracker not only has a strong capability to learn more effective object representations, but also is computational and memory storage efficiency, either during the training or testing stages. To the best of our knowledge, we are the first to propose this approach. Without bells and whistles, experimental results demonstrate that our approach achieves state-of-the-art performance on several recent benchmark datasets, at a speed far exceeding 27 frames per second. with real-time speed are widely pursued in numerous applications, including video surveillance, autonomous driving, pose estimation, and human-computer interfaces. Although considerable progress has been made by many tracking approaches in the last decade, visual tracking is still a challenging problem due to multiple negative scenarios such as occlusions, fast motions, scale variations, and background clutters [1,2,3,4].Most recent tracking approaches are developed based on two successful frameworks. The first one is the discriminative correlation filter (DCF). Thanks to the circular shifting of training samples and fast learning correlation filters in the Fourier frequency domain, DCF demonstrates superior computational efficiency and fairly good tracking accuracy and has attracted increasing attention since MOSSE [5]first exploited it. Later, in order to overcome different kinds of challenges and achieve competitive tracking performance, the advancement in DCF-based tracking is focused on the use of kernel functions [6], motion information [7,8], multi-dimensional features [9,10], multi-scale estimation [11,12], boundary effects allevi...

show abstract

“…As illustrated in Fig. 8, we compare the precision plots and success plots obtained by our OA-LSTM-ADA and several state-of-the-art trackers including MemTrack [50], TRACA [57], SiamFC-tri [38], CFNet2-tri [38], ACFN [58], CNN-SVM [59], DLSSVM [60], SiamFC [5], CFNet [9], CSR-DCF [61], Staple [30], RFL [10], KCF [29] and CNT [62]. We choose these methods because SiamFC, CFNet, SiamFC-tri and CFNet2-tri are Siamese network based tracking methods, which are closely related to our OA-LSTM-ADA (recall that OA-LSTM-ADA utilizes the Siamese network to pre-estimate the densely sampled proposals).…”

Section: Quantitative Comparisonmentioning

confidence: 99%

Object-adaptive LSTM network for real-time visual tracking with adversarial data augmentation

Yan

Chen

et al. 2020

Neurocomputing

View full text Add to dashboard Cite

In recent years, deep learning based visual tracking methods have obtained great success owing to the powerful feature representation ability of Convolutional Neural Networks (CNNs). Among these methods, classification-based tracking methods exhibit excellent performance while their speeds are heavily limited by the expensive computation for massive proposal feature extraction. In contrast, matching-based tracking methods (such as Siamese networks) possess remarkable speed superiority.However, the absence of online updating renders these methods unadaptable to significant object appearance variations. In this paper, we propose a novel real-time visual tracking method, which adopts an object-adaptive LSTM network to effectively capture the video sequential dependencies and adaptively learn the object appearance variations. For high computational efficiency, we also present a fast proposal selection strategy, which utilizes the matching-based tracking method to pre-estimate dense proposals and selects high-quality ones to feed to the LSTM network for classification. This strategy efficiently filters out some irrelevant proposals and avoids the redundant computation for feature extraction, which enables our method to operate faster than conventional classification-based tracking methods. In addition, to handle the problems of sample inadequacy and class imbalance during online tracking, we adopt a data augmentation technique based on the Generative Adversarial Network (GAN) to facilitate the training of the LSTM network. Extensive experiments on four visual tracking benchmarks demonstrate the state-of-the-art performance of our method in terms of both tracking accuracy and speed, which exhibits great potentials of recurrent structures for visual tracking.

show abstract

Object Tracking via Dual Linear Structured SVM and Explicit Feature Map

Cited by 231 publications

References 25 publications

A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

Siamese attentional keypoint network for high performance visual tracking

Object-adaptive LSTM network for real-time visual tracking with adversarial data augmentation

Contact Info

Product

Resources

About