Visual tracking using Locality-constrained Linear Coding and saliency map for visible light and infrared image sequences

Ding, Meng; Yao, Yuheng; Li, Wei; Cao, Yunfeng

doi:10.1016/j.image.2018.06.019

Cited by 8 publications

(5 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…s s e B (12) where s 1 ×s 2 is the size of the response map, B is a binary image computed by segmenting the response map using the threshold as θ of the maximum response value. Output: Compute e using Eq.…”

Section: Fusion Scheme Of Response Mapsmentioning

confidence: 99%

See 1 more Smart Citation

Thermal Infrared Single-Pedestrian Tracking for Advanced Driver Assistance System

Ding

Chen

Cao

2023

IEEE Trans. Intell. Veh.

Self Cite

View full text Add to dashboard Cite

Tracking algorithms with low computational complexity and reliable performance are important in developing advanced driver assistance systems (DAS). This paper proposes a method of single-pedestrian tracking using thermal infrared cameras to meet the needs of DAS operating in nighttime and lowvisibility conditions. The proposed algorithm uses the background-aware correlation filter (BACF) as the basic tracking framework. In order to address the problem that directly introducing the convolutional features leads to tracking performance degradation in the BACF framework, this paper proposes a fusion scheme to integrate handcrafted and convolutional features to make full use of the advantages of both the features. The proposed scheme combines response maps from convolutional and handcrafted features through fusion coefficients to improve the performance of the trackers based on the single features. In order to calculate fusion coefficients, a novel approach of searching the main peak and interference peaks of a response map is proposed by using local binary pattern values of the response map to locate all local maximum points. Experimental results show that the proposed algorithm outperforms the existing 9 competing tracking algorithms and can be used in vehicle platforms as a module of DAS to improve the safe level of driving in nighttime.

show abstract

Section: Fusion Scheme Of Response Mapsmentioning

confidence: 99%

“…Generic tracking algorithms are to estimate the trajectory of a target throughout a sequence of image frames when only the location of a target in the first frame is known [12]. In the field of intelligent transportation systems, visual tracking is used for road traffic surveillance and self-driving vehicle including V-DAS.…”

Section: Introductionmentioning

confidence: 99%

Thermal Infrared Single-Pedestrian Tracking for Advanced Driver Assistance System

Ding

Chen

Cao

2023

IEEE Trans. Intell. Veh.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Because the fusion of RGB and TIR data more easily achieves all-weather object tracking in the open environment, the researches on RGB-T object tracking methods become more and more popular. From the perspective of data fusion, the RGB-T object tracking framework can be roughly divided into traditional methods [ 38 , 39 ], sparse representation (SR)-based [ 40 , 41 , 42 , 43 , 44 ], graph-based [ 45 , 46 , 47 ], correlation filter-based [ 48 , 49 , 50 , 51 ], and deep learning-based approaches. Earlier studies used manual features to perform the appearance modeling of the target object.…”

Section: Related Workmentioning

confidence: 99%

Channel Exchanging for RGB-T Tracking

Zhao

Zhu

Ren

et al. 2021

Sensors

View full text Add to dashboard Cite

It is difficult to achieve all-weather visual object tracking in an open environment only utilizing single modality data input. Due to the complementarity of RGB and thermal infrared (TIR) data in various complex environments, a more robust object tracking framework can be obtained using video data of these two modalities. The fusion methods of RGB and TIR data are the core elements to determine the performance of the RGB-T object tracking method, and the existing RGB-T trackers have not solved this problem well. In order to solve the current low utilization of information intra single modality in aggregation-based methods and between two modalities in alignment-based methods, we used DiMP as the baseline tracker to design an RGB-T object tracking framework channel exchanging DiMP (CEDiMP) based on channel exchanging. CEDiMP achieves dynamic channel exchanging between sub-networks of different modes hardly adding any parameters during the feature fusion process. The expression ability of the deep features generated by our data fusion method based on channel exchanging is stronger. At the same time, in order to solve the poor generalization ability of the existing RGB-T object tracking methods and the poor ability in the long-term object tracking, more training of CEDiMP on the synthetic dataset LaSOT-RGBT is added. A large number of experiments demonstrate the effectiveness of the proposed model. CEDiMP achieves the best performance on two RGB-T object tracking benchmark datasets, GTOT and RGBT234, and performs outstandingly in the generalization testing.

show abstract

“…As a representative of random sampling, particle sampling is based on Monte Carlo methodology. Since both the computational burden and tracking accuracy are proportional to the particle number, real-time performance is always a huge challenge for particle filter-based trackers [22], [23]. In tracking tasks, dense sampling is to collect all the subwindows with a certain step size in the target's neighborhood.…”

Section: A Sampling Scheme For Trackingmentioning

confidence: 99%

Visual Tracking With Online Assessment and Improved Sampling Strategy

et al. 2020

Self Cite

View full text Add to dashboard Cite

The kernelized correlation filter (KCF) is one of the most successful trackers in computer vision today. However its performance may be significantly degraded in a wide range of challenging conditions such as occlusion and out of view. For many applications, particularly safety critical applications (e.g. autonomous driving), it is of profound importance to have consistent and reliable performance during all the operation conditions. This paper addresses this issue of the KCF based trackers by the introduction of two novel modules, namely online assessment of response map, and a strategy of combining cyclically shifted sampling with random sampling in deep feature space. A method of online assessment of response map is proposed to evaluate the tracking performance by constructing a 2-D Gaussian estimation model. Then a strategy of combining cyclically shifted sampling with random sampling in deep feature space is presented to improve the tracking performance when the tracking performance is assessed to be unreliable based on the response map. Therefore, the module of online assessment can be regarded as the trigger for the second module. Experiments verify the tracking performance is significantly improved particularly in challenging conditions as demonstrated by both quantitative and qualitative comparisons of the proposed tracking algorithm with the state-of-the-art tracking algorithms on OTB-2013 and OTB-2015 datasets.INDEX TERMS Visual tracking, kernelized correlation filter, online assessment, random sampling, deep feature, handcrafted feature.

show abstract

Visual tracking using Locality-constrained Linear Coding and saliency map for visible light and infrared image sequences

Cited by 8 publications

References 36 publications

Thermal Infrared Single-Pedestrian Tracking for Advanced Driver Assistance System

Thermal Infrared Single-Pedestrian Tracking for Advanced Driver Assistance System

Channel Exchanging for RGB-T Tracking

Visual Tracking With Online Assessment and Improved Sampling Strategy

Contact Info

Product

Resources

About