Infrared dim moving target tracking via sparsity-based discriminative classifier and convolutional network

Qian, Kun; Zhou, Huixin; Wang, Bingjian; Song, Shangzhen; Zhao, Dong

doi:10.1016/j.infrared.2017.09.003

Cited by 9 publications

(7 citation statements)

References 19 publications

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“…Li et al [28] constructed image patches sparsely and combined a particle filter to detect small targets from an image sequence. In order to eliminate the negative influence of clutter in image patches, Qian et al [29] adopted a guided filter and Gaussian weight to suppress the cluttered background. Dong et al [30] first estimated the motion of the imaging platform using image patch correlation and then detected the true target trajectory based on the trajectory continuity.…”

Section: ) Image Patch Association Based Methodsmentioning

confidence: 99%

Small Aerial Target Detection for Airborne Infrared Detection Systems Using LightGBM and Trajectory Constraints

Sun

Guo

Zhang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Factors, such as rapid relative motion, clutter background, etc., make robust small aerial target detection for airborne infrared detection systems a challenge. Existing methods are facing difficulties when dealing with such cases. We consider that a continuous and smooth trajectory is critical in boosting small infrared aerial target detection performance. A simple and effective small aerial target detection method for airborne infrared detection system using LightGBM and trajectory constraints is proposed in this paper. First, we simply formulate target candidate detection as a binary classification problem. Target candidates in every individual frame are detected via interesting pixel detection and a trained LightGBM model. Then, the local smoothness and global continuous characteristic of the target trajectory are modeled as short-strict and long-loose constraints. The trajectory constraints are used efficiently for detecting the true small infrared aerial targets from numerous target candidates. Experiments on public datasets demonstrate that the proposed method performs better than other existing methods. Furthermore, a public dataset for small aerial target detection in airborne infrared detection systems is constructed (https://small-infrared-aerial-target-detection.grand-challenge.or g/). To the best of our knowledge, this dataset has the largest data scale and richest scene types within this field.

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Section: ) Image Patch Association Based Methodsmentioning

confidence: 99%

Small Aerial Target Detection for Airborne Infrared Detection Systems Using LightGBM and Trajectory Constraints

Sun

Guo

Zhang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In [28], a discriminative sparse representation model is presented for infrared dim moving target tracking, in which the dictionary is composed of a target dictionary and a background dictionary. A sparsity-based discriminative classifier is proposed in [9] to evaluate the confidence of different target templates, of which the best template is used for calculating the convolution score of the candidate images. To explore the underlying relationship of multiple candidates, a low-rank sparse learning method is proposed in [13] that describes corruptions adaptively by finding the maximum-likelihood estimation solution of the residuals.…”

Section: Sparse Representation-based Tir Tracking Methodsmentioning

confidence: 99%

“…+ λ q e 1 (9) where U represents the eigenbasis vectors and p is the new observation. The new entrant q is the target area removing noises and occlusion.…”

Section: Algorithmmentioning

confidence: 99%

“…To handle these difficulties, several TIR tracking methods have been proposed, which can be categorized into discriminative tracking methods [5][6][7][8][9][10][11] and generative tracking methods [12][13][14][15][16][17][18]. Discriminative approaches formulate tracking as a classification task, which aims to find the target area whose features are most discriminative to the background.…”

Section: Introductionmentioning

confidence: 99%

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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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“…Zhang et al proposed a lightweight convolutional network structure, termed as CNT, to extract object features [35], and a bank of local image patches of both object and background regions are chosen as the filters to generate a set of feature maps of an object. Then, Qian et al employed both CNT and a sparsity-based discriminative classifier to track infrared dim target [36]. The merit of CNT is that it can encode local structural information of an object with simple two-layer convolutional networks without training.…”

Section: Introductionmentioning

confidence: 99%

Object Tracking Based on Compressive Features and Extreme Learning Machine

2019

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Effective object tracking is a great challenging task in computer vision due to the appearance changes of an object. Extreme learning machine (ELM) can analytically determine the weights of feed-forward neural networks, while compressive features can be extracted via a random sparse matrix. Thus, their common advantages are running fast. Moreover, the strong learning ability of ELM is helpful to make a tracker robust target appearance change. In this paper, we propose an effective tracking method using ELM and compressive features. First, a bank of compressive Haar-like features is extracted with the sparse random matrix. To make the feature invariant to illumination, the mean illumination of each image patch is removed from each Haar-like feature. Second, a bank of ELM-based weak classifiers are combined together to form a strong classifier to complete tracking task. Considering the discrimination performance of features is uneven, the training accuracy of each ELM is employed as the confidence (weight) of the weak classifier. Thus, less discriminative features will be weakened. The proposed tracker has been tested on several representative sequences, and the results show that it achieves the highest tracking success rate and the tracking accuracy among the compared trackers including the FCT, CNT, MIL, KCF, and ESPT. INDEX TERMSExtreme learning machine, training accuracy, compressive features, object tracking.

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Infrared dim moving target tracking via sparsity-based discriminative classifier and convolutional network

Cited by 9 publications

References 19 publications

Small Aerial Target Detection for Airborne Infrared Detection Systems Using LightGBM and Trajectory Constraints

Small Aerial Target Detection for Airborne Infrared Detection Systems Using LightGBM and Trajectory Constraints

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

Object Tracking Based on Compressive Features and Extreme Learning Machine

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