Evaluation of Feature Channels for Correlation-Filter-Based Visual Object Tracking in Infrared Spectrum

Gundogdu, Erhan; Koç, Aykut; Solmaz, Berkan; Hammoud, Riad I.; Alatan, A. Aydın

doi:10.1109/cvprw.2016.43

Cited by 32 publications

(19 citation statements)

References 29 publications

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“…In [7], features are extracted from multiple convolutional layers and are used to construct multiple weak trackers to give response maps of the target's location. The evaluation result in [27] has shown that the learned infrared features perform favorably against the hand-crafted features (HOG and Gist) in the correlation filter-based tracking framework.…”

Section: Deep Learning-based Tir Tracking Methodsmentioning

confidence: 99%

Mask Sparse Representation Based on Semantic Features for Thermal Infrared Target Tracking

Peng

Chen

et al. 2019

Remote Sensing

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Thermal infrared (TIR) target tracking is a challenging task as it entails learning an effective model to identify the target in the situation of poor target visibility and clutter background. The sparse representation, as a typical appearance modeling approach, has been successfully exploited in the TIR target tracking. However, the discriminative information of the target and its surrounding background is usually neglected in the sparse coding process. To address this issue, we propose a mask sparse representation (MaskSR) model, which combines sparse coding together with high-level semantic features for TIR target tracking. We first obtain the pixel-wise labeling results of the target and its surrounding background in the last frame, and then use such results to train target-specific deep networks using a supervised manner. According to the output features of the deep networks, the high-level pixel-wise discriminative map of the target area is obtained. We introduce the binarized discriminative map as a mask template to the sparse representation and develop a novel algorithm to collaboratively represent the reliable target part and unreliable target part partitioned with the mask template, which explicitly indicates different discriminant capabilities by label 1 and 0. The proposed MaskSR model controls the superiority of the reliable target part in the reconstruction process via a weighted scheme. We solve this multi-parameter constrained problem by a customized alternating direction method of multipliers (ADMM) method. This model is applied to achieve TIR target tracking in the particle filter framework. To improve the sampling effectiveness and decrease the computation cost at the same time, a discriminative particle selection strategy based on kernelized correlation filter is proposed to replace the previous random sampling for searching useful candidates. Our proposed tracking method was tested on the VOT-TIR2016 benchmark. The experiment results show that the proposed method has a significant superiority compared with various state-of-the-art methods in TIR target tracking.

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Section: Deep Learning-based Tir Tracking Methodsmentioning

confidence: 99%

Mask Sparse Representation Based on Semantic Features for Thermal Infrared Target Tracking

Peng

Chen

et al. 2019

Remote Sensing

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“…Existing deep TIR trackers usually use the pre-trained feature for representation and combine it with conventional frameworks for tracking. DSST-tir (Gundogdu et al 2016) (Gundogdu et al 2016) uses a small TIR dataset to train a classification network for feature extraction and then combines it with the DSST tracker for TIR tracking. ECO-tir (Zhang et al 2019) explores a Generative Adversarial Network (GAN) to generate synthetic TIR images and then uses them to train a Siamese network for feature extraction.…”

Section: Related Workmentioning

confidence: 99%

“…These methods can be roughly divided into two categories, deep feature based TIR trackers and matching-based deep TIR trackers. Deep feature based TIR trackers, e.g., DSSTtir (Gundogdu et al 2016), MCFTS (Liu et al 2017), and LMSCO (Gao et al 2018), use a pre-trained classification network for extracting deep features and then integrate them into conventional trackers. Despite the demonstrated success, their performance is limited by the pre-trained deep features which are learned from RGB images and are less effective in representing TIR objects.…”

Section: Introductionmentioning

confidence: 99%

Multi-Task Driven Feature Models for Thermal Infrared Tracking

et al. 2020

AAAI

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Existing deep Thermal InfraRed (TIR) trackers usually use the feature models of RGB trackers for representation. However, these feature models learned on RGB images are neither effective in representing TIR objects nor taking fine-grained TIR information into consideration. To this end, we develop a multi-task framework to learn the TIR-specific discriminative features and fine-grained correlation features for TIR tracking. Specifically, we first use an auxiliary classification network to guide the generation of TIR-specific discriminative features for distinguishing the TIR objects belonging to different classes. Second, we design a fine-grained aware module to capture more subtle information for distinguishing the TIR objects belonging to the same class. These two kinds of features complement each other and recognize TIR objects in the levels of inter-class and intra-class respectively. These two feature models are learned using a multi-task matching framework and are jointly optimized on the TIR tracking task. In addition, we develop a large-scale TIR training dataset to train the network for adapting the model to the TIR domain. Extensive experimental results on three benchmarks show that the proposed algorithm achieves a relative gain of 10% over the baseline and performs favorably against the state-of-the-art methods. Codes and the proposed TIR dataset are available at https://github.com/QiaoLiuHit/MMNet.

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“…Traditional discriminative methods are also often used for object tracking in thermal domain. Recently, the most often employed tracking algorithms have been correlation filters that can be used with different types of features 7,8,20‐22 . Correlation filters are one type of discriminative tracking methods.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, the most often employed tracking algorithms have been correlation filters that can be used with different types of features. 7,8,[20][21][22] Correlation filters are one type of discriminative tracking methods. They are not computationally demanding, which is certainly an advantage, but they encounter all the problems that other algorithms have to cope with, such as occlusions, background merging, low contrast, and the like.…”

mentioning

confidence: 99%

Robust tracking of moving objects using thermal camera and speeded up robust features descriptor

Vlahovic

Djurović

2020

Adaptive Control & Signal

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Robust methods based on nonlinear influence functions are often used to remove outliers from data. The article describes the design of an algorithm for tracking a moving object in a thermal image using a SURF descriptor and robust Kalman filter. However, given that the main shortfall of robust methods is reduced efficiency, tunable parameters of the robust influence function are used to achieve a balance between robustness and efficiency. The parameters adapt to the observed scene's current conditions, while their estimation is based on the calculated data outlier contamination factor. The outliers that contaminate the data are generally a result of various types of occlusions. The results show that the proposed solution achieves better performance than the standard Kalman filter or fixed-parameter robust Kalman filter.

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Evaluation of Feature Channels for Correlation-Filter-Based Visual Object Tracking in Infrared Spectrum

Cited by 32 publications

References 29 publications

Mask Sparse Representation Based on Semantic Features for Thermal Infrared Target Tracking

Mask Sparse Representation Based on Semantic Features for Thermal Infrared Target Tracking

Multi-Task Driven Feature Models for Thermal Infrared Tracking

Robust tracking of moving objects using thermal camera and speeded up robust features descriptor

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