Severely Blurred Object Tracking by Learning Deep Image Representations

Ding, Jianwei; Huang, Yongzhen; Liu, Wei; Huang, Kaiqi

doi:10.1109/tcsvt.2015.2406231

Cited by 31 publications

(31 citation statements)

References 23 publications

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“…The motivation is intuitive: in the scenarios of shot changes or scene cuts, the temporal coherence (from previous frame) becomes weaker and the tracker needs to assign more weight to the most adjacent (or neighboring) frame to better capture the instantaneous information. The second modification is to incorporate a shot change detector (e.g., [76], [77]) into our K(MF) 2 JMT, such that the system can automatically detect the shot changes. Once a shot change is confirmed, the system needs to re-detect or re-identify the location of the target.…”

Section: Appendix Amentioning

confidence: 99%

“…V ISUAL tracking is one of the most important components in computer vision system. It has been widely used in visual surveillance, human computer interaction, and robotics [1], [2]. Given an annotation of the object (bounding box) in the first frame, the task of visual tracking is to estimate the target locations and scales in subsequent video frames.…”

Section: Introductionmentioning

confidence: 99%

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Robust Visual Tracking Using Multi-Frame Multi-Feature Joint Modeling

Zhang

et al. 2019

IEEE Trans. Circuits Syst. Video Technol.

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It remains a huge challenge to design effective and efficient trackers under complex scenarios, including occlusions, illumination changes and pose variations. To cope with this problem, a promising solution is to integrate the temporal consistency across consecutive frames and multiple feature cues in a unified model. Motivated by this idea, we propose a novel correlation filter-based tracker in this work, in which the temporal relatedness is reconciled under a multi-task learning framework and the multiple feature cues are modeled using a multi-view learning approach. We demonstrate the resulting regression model can be efficiently learned by exploiting the structure of blockwise diagonal matrix. A fast blockwise diagonal matrix inversion algorithm is developed thereafter for efficient online tracking. Meanwhile, we incorporate an adaptive scale estimation mechanism to strengthen the stability of scale variation tracking. We implement our tracker using two types of features and test it on two benchmark datasets. Experimental results demonstrate the superiority of our proposed approach when compared with other state-of-the-art trackers. MATLAB code is available from our project homepage http://bmal.hust.edu.cn/ project/KMF2JMTtracking.html.

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Section: Appendix Amentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Visual Tracking Using Multi-Frame Multi-Feature Joint Modeling

Zhang

et al. 2019

IEEE Trans. Circuits Syst. Video Technol.

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“…In this study [22], the authors introduced an activity classification system based on activity class through random forests (RFs) classifier. Moreover, in [23], the authors discussed a human-centered perspective to develop an intelligent vehicle.…”

Section: Related Workmentioning

confidence: 99%

V-ITS: Video-based Intelligent Transportation System for Monitoring Vehicle Illegal Activities

Abbas¹

2019

ijacsa

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Vehicle monitoring is a challenging task for videobased intelligent transportation system (V-ITS). Nowadays, the V-ITS system has a significant socioeconomic impact on the development of smart cities and always demand to monitor different traffic parameters. It noticed that traffic accidents are exceeded throughout the world with the percentage of 1.7%. The increase in accidents and the percentage of deaths are due to the people that don't abide by the traffic rules. To address these challenges, an improved V-ITS system is developed in this paper to detect and track vehicles and driver's activities during highway driving. This improved V-ITS system is capable to do automatic traffic management that saves traffic accidents. It provides the feature of a real-time detection algorithm for driver immediate line overrun, speed limit overrun and yellow-line driving. To develop this V-ITS system, a pre-trained convolutional neural network (CNN) model with 4-layer architecture was developed and then deep-belief network (DBN) model was utilized to recognize illegal activities. To implement V-ITS system, OpenCV and python tools are mainly utilized. The GRAM-RTM online free data sets were used to test the performance of V-ITS system. The overall significance of this intelligent V-ITS system is comparable to other state-of-the-art systems. The real-time experimental results indicate that the V-ITS system can be used to reduce the number of accidents and ensure the safety of passengers as well as pedestrians.

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“…Traffic flow prediction and deep learning approach is been proposed in [52]. Object tracking in blurred videos using blurred videos and deep image representations is proposed by Jianwei Ding et al [53]. Adaptively learn representation that is more effective for the task of vehicle color recognition using spatial pyramid deep learning is given by Chuanping Hu et al [54].…”

Section: Related Workmentioning

confidence: 99%

A novel training algorithm for convolutional neural network

Anuse¹,

Vyas²

2016

Complex Intell. Syst.

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Many machine learning softwares are available which help the researchers to accomplish various tasks. These software packages have various conventional algorithms which perform well if the training and test data are independent and identically distributed. However, this might not be the case in the real world. The training data may not be available at one time. In the case of neural networks, the architecture has to be retrained with new data that are made available subsequently. In this paper, we present a novel training algorithm which can avoid complete retraining of any neural network architecture meant for visual pattern recognition. To show the utility of the algorithm, we have investigated the performance of convolutional neural network (CNN) architecture for a face recognition task under transfer learning. The proposed training algorithm may be used for enhancing the utility of machine learning software by providing researchers with an approach that can reduce the training time under transfer learning.

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Severely Blurred Object Tracking by Learning Deep Image Representations

Cited by 31 publications

References 23 publications

Robust Visual Tracking Using Multi-Frame Multi-Feature Joint Modeling

Robust Visual Tracking Using Multi-Frame Multi-Feature Joint Modeling

V-ITS: Video-based Intelligent Transportation System for Monitoring Vehicle Illegal Activities

A novel training algorithm for convolutional neural network

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