Object Tracking Algorithm Implementation for Security Applications

Sivanantham, S.; Paul, N. Nitin; Iyer, Rithvika

doi:10.17654/ec016010001

Cited by 19 publications

(5 citation statements)

References 7 publications

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“…FPGAs' implementations for this technique include histogram equalization [714][715][716][717], retinex image enhancement [718][719][720], Infrared Focal Plane Arrays (IRFPA) image enhancement [721,722] and telescopes' atmospheric turbulence mitigation [723,724]. Other image processing applications that use FPGAs are image segmentation [725][726][727][728][729], object tracking [730][731][732][733][734], object detection [735][736][737][738][739] (real time object detection [740][741][742], moving objects detection [743][744][745], pedestrian detection [746][747][748][749] and background identification [750][751][752]), optical flow [753][754][755][756][757] and Hough transform [758]…”

Section: Image and Video Processingmentioning

confidence: 99%

Field Programmable Gate Array Applications—A Scientometric Review

2019

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Field Programmable Gate Array (FPGA) is a general purpose programmable logic device that can be configured by a customer after manufacturing to perform from a simple logic gate operations to complex systems on chip or even artificial intelligence systems. Scientific publications related to FPGA started in 1992 and, up to now, we found more than 70,000 documents in the two leading scientific databases (Scopus and Clarivative Web of Science). These publications show the vast range of applications based on FPGAs, from the new mechanism that enables the magnetic suspension system for the kilogram redefinition, to the Mars rovers’ navigation systems. This paper reviews the top FPGAs’ applications by a scientometric analysis in ScientoPy, covering publications related to FPGAs from 1992 to 2018. Here we found the top 150 applications that we divided into the following categories: digital control, communication interfaces, networking, computer security, cryptography techniques, machine learning, digital signal processing, image and video processing, big data, computer algorithms and other applications. Also, we present an evolution and trend analysis of the related applications.

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Section: Image and Video Processingmentioning

confidence: 99%

Field Programmable Gate Array Applications—A Scientometric Review

2019

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“…The escalation of high specification computers, high-resolution reasonable cameras, and highly dependent video analysis-based applications drive research in object tracking. To date, object tracking is pertinent to the tasks of motion-based recognition, automated surveillance [1], video captioning [2], humancomputer interaction [3], traffic monitoring [4], and autonomous vehicles [5,6].…”

Section: Introductionmentioning

confidence: 99%

Visual Object Tracking Fusing CNN and Color Histogram based Tracker and Depth Estimation for Automatic Immersive Audio Mixing

Park¹,

Islam²,

Baek³

2020

KSII TIIS

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We propose a robust visual object tracking algorithm fusing a convolutional neural network tracker trained offline from a large number of video repositories and a color histogram based tracker to track objects for mixing immersive audio. Our algorithm addresses the problem of occlusion and large movements of the CNN based GOTURN generic object tracker. The key idea is the offline training of a binary classifier with the color histogram similarity values estimated via both trackers used in this method to opt appropriate tracker for target tracking and update both trackers with the predicted bounding box position of the target to continue tracking. Furthermore, a histogram similarity constraint is applied before updating the trackers to maximize the tracking accuracy. Finally, we compute the depth(z) of the target object by one of the prominent unsupervised monocular depth estimation algorithms to ensure the necessary 3D position of the tracked object to mix the immersive audio into that object. Our proposed algorithm demonstrates about 2% improved accuracy over the outperforming GOTURN algorithm in the existing VOT2014 tracking benchmark. Additionally, our tracker also works well to track multiple objects utilizing the concept of single object tracker but no demonstrations on any MOT benchmark.

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“…Visual tracking is the process of Spatio-temporal localization of a moving object in the camera scene. Object localization has potential applications, including human activity recognition [1], vehicle navigation [2], surveillance and security [3], and human-machine interaction [4]. The researchers are developing robust trackers to reduce the computational cost of the visual object tracking algorithm.…”

Section: Introductionmentioning

confidence: 99%

Low-Rank Multi-Channel Features for Robust Visual Object Tracking

et al. 2019

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Kernel correlation filters (KCF) demonstrate significant potential in visual object tracking by employing robust descriptors. Proper selection of color and texture features can provide robustness against appearance variations. However, the use of multiple descriptors would lead to a considerable feature dimension. In this paper, we propose a novel low-rank descriptor, that provides better precision and success rate in comparison to state-of-the-art trackers. We accomplished this by concatenating the magnitude component of the Overlapped Multi-oriented Tri-scale Local Binary Pattern (OMTLBP), Robustness-Driven Hybrid Descriptor (RDHD), Histogram of Oriented Gradients (HoG), and Color Naming (CN) features. We reduced the rank of our proposed multi-channel feature to diminish the computational complexity. We formulated the Support Vector Machine (SVM) model by utilizing the circulant matrix of our proposed feature vector in the kernel correlation filter. The use of discrete Fourier transform in the iterative learning of SVM reduced the computational complexity of our proposed visual tracking algorithm. Extensive experimental results on Visual Tracker Benchmark dataset show better accuracy in comparison to other state-of-the-art trackers.

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Object Tracking Algorithm Implementation for Security Applications

Cited by 19 publications

References 7 publications

Field Programmable Gate Array Applications—A Scientometric Review

Field Programmable Gate Array Applications—A Scientometric Review

Visual Object Tracking Fusing CNN and Color Histogram based Tracker and Depth Estimation for Automatic Immersive Audio Mixing

Low-Rank Multi-Channel Features for Robust Visual Object Tracking

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