Object Tracking Based on Deep CNN Feature and Color Feature

Qi, Yujuan; Wang, Yanjiang; Liu, Yu-Chi

doi:10.1109/icsp.2018.8652470

Cited by 9 publications

(7 citation statements)

References 9 publications

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“…CNNs have proved successful in many machine learning tasks such as handwriting recognition [ 17 ], natural language processing [ 18 ], text classification [ 19 ], image classification [ 17 ], face recognition [ 20 ], face detection [ 21 ], object detection [ 22 ], video classification [ 23 ], object tracking [ 24 ], super resolution [ 25 ], human pose estimation [ 26 ], and so forth. CNNs, introduced by Lecun et al [ 17 ], combine three architectural concepts of local receptive fields, shared weights, and spatial or temporal subsampling in order to ensure some degree of shift, scale, and distortion invariance.…”

Section: Proposed Methodsmentioning

confidence: 99%

Optimizing Deep CNN Architectures for Face Liveness Detection

Koshy

Mahmood

2019

Entropy

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Face recognition is a popular and efficient form of biometric authentication used in many software applications. One drawback of this technique is that it is prone to face spoofing attacks, where an impostor can gain access to the system by presenting a photograph of a valid user to the sensor. Thus, face liveness detection is a necessary step before granting authentication to the user. In this paper, we have developed deep architectures for face liveness detection that use a combination of texture analysis and a convolutional neural network (CNN) to classify the captured image as real or fake. Our development greatly improved upon a recent approach that applies nonlinear diffusion based on an additive operator splitting scheme and a tridiagonal matrix block-solver algorithm to the image, which enhances the edges and surface texture in the real image. We then fed the diffused image to a deep CNN to identify the complex and deep features for classification. We obtained 100% accuracy on the NUAA Photograph Impostor dataset for face liveness detection using one of our enhanced architectures. Further, we gained insight into the enhancement of the face liveness detection architecture by evaluating three different deep architectures, which included deep CNN, residual network, and the inception network version 4. We evaluated the performance of each of these architectures on the NUAA dataset and present here the experimental results showing under what conditions an architecture would be better suited for face liveness detection. While the residual network gave us competitive results, the inception network version 4 produced the optimal accuracy of 100% in liveness detection (with nonlinear anisotropic diffused images with a smoothness parameter of 15). Our approach outperformed all current state-of-the-art methods.

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Section: Proposed Methodsmentioning

confidence: 99%

Optimizing Deep CNN Architectures for Face Liveness Detection

Koshy

Mahmood

2019

Entropy

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“…A convolutional neural network (CNN) has great advantages in representing visual data compared with traditional model-based and feature-based tracking algorithms, and they have been widely used in various computer vision tasks, such as image classification, semantic segmentation, and so on [31][32][33][34]. However, it is not easily applicable in visual tracking, since it is difficult to obtain useful datasets including diverse combination of targets and backgrounds with different appearance, and motion modes of different categories of targets in different video sequences.…”

Section: Moving-target-tracking Methods Based On Mdnetmentioning

confidence: 99%

Sea-Surface Target Visual Tracking with a Multi-Camera Cooperation Approach

Rao

Chen

et al. 2022

Sensors

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Cameras are widely used in the detection and tracking of moving targets. Compared to target visual tracking using a single camera, cooperative tracking based on multiple cameras has advantages including wider visual field, higher tracking reliability, higher precision of target positioning and higher possibility of multiple-target visual tracking. With vast ocean and sea surfaces, it is a challenge using multiple cameras to work together to achieve specific target tracking and detection, and it will have a wide range of application prospects. According to the characteristics of sea-surface moving targets and visual images, this study proposed and designed a sea-surface moving-target visual detection and tracking system with a multi-camera cooperation approach. In the system, the technologies of moving target detection, tracking, and matching are studied, and the strategy to coordinate multi-camera cooperation is proposed. The comprehensive experiments of cooperative sea-surface moving-target visual tracking show that the method used in this study has improved performance compared with contrapositive methods, and the proposed system can meet the needs of multi-camera cooperative visual tracking of moving targets on the sea surface.

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“…In recent research, deep CNN features are combined with its colour histogram to improve the object tracking performance conducted by Yujuan Qi et al [22]. An adaptive particle filter serves as the main tracker.…”

Section: Deep Learning Based Methodsmentioning

confidence: 99%

“…VTB V1.0 2017 G. Ning [24] The tracker is more accurate and robust while maintaining low computational cost. ImageNet -PET 2017 PF CNN C [22] The tracker deals with serious object occlusions and appearance changes more effectively. VTB 2018…”

Section: Pet 2013mentioning

confidence: 99%

A motion parameters estimating method based on deep learning for visual blurred object tracking

Iraei

Faez

2021

IET Image Processing

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Tracking the specific object in the blurred scenes is one of the challenging problems in computer vision and image processing. The accuracy and performance of trackers within the blur frames usually demonstrate a severe decrease. Accordingly, this problem needs to be corrected for better tracking results. Thus, this study seeks to present the best solution. To this end, a novel deep learning approach is proposed for object tracking in the presence of motion blur and fast motion. The hidden information in the blurring kernel is useful for tracking a specific blurred object through a series of consecutive frames. In this study, this matter is evaluated from a new perspective to solve the problems of blurred object tracking and objects with highly fast motions using a convolutional neural network (CNN) and a particle filter. Therefore, the proposed framework has two phases. First, the kernel leading to blurring is estimated by CNN, and then by a particle filter and the probability distribution of motion information that has been achieved by the kernel estimation the object is tracked. The results demonstrate that the suggested method can enhance the accuracy of tracking compared with the state-of-the-art, especially when the amount of blur is high.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Object Tracking Based on Deep CNN Feature and Color Feature

Cited by 9 publications

References 9 publications

Optimizing Deep CNN Architectures for Face Liveness Detection

Optimizing Deep CNN Architectures for Face Liveness Detection

Sea-Surface Target Visual Tracking with a Multi-Camera Cooperation Approach

A motion parameters estimating method based on deep learning for visual blurred object tracking

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