An Adaptive Video Acquisition Scheme for Object Tracking and Its Performance Optimization

Banerjee, Srutarshi; Chopp, Henry; Serra, Juan G.; Yang, Hao; Cossairt, Oliver; Katsaggelos, Aggelos K.

doi:10.1109/jsen.2021.3081351

Cited by 6 publications

(3 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various methodologies are being introduced, considering different forms of use cases. The work carried out by Banerjee et al [56] has developed a hardware design of architecture for the acquisition of Video. The study model uses dynamic programming towards the reduction of normalized area for the allocation of resources.…”

Section: Miscellaneous Approachesmentioning

confidence: 99%

“…Some of the significant approaches noted for this approach are adaptive frame-based tracking [110][111], construction of forward and backward trackers [112], adoption of refinement network for integrated Detection and tracking [113], and convolution network-based tracking [114]. Apart from the above-mentioned evolving trends of approaches, there are also miscellaneous methods [56]- [66]. The analysis of this approach is further carried out from the perspective of timebased evolution.…”

Section: Research Trend Analysismentioning

confidence: 99%

See 1 more Smart Citation

Methodological Insights Towards Leveraging Performance in Video Object Tracking and Detection

Kurian

2023

IJACSA

View full text Add to dashboard Cite

Video Object Detection and Tracking (VODT), one of its integral operations of surveillance system in present time, mechanizes a way to identify and track the target object autonomously and seamlessly within its visual field. However, the challenges associated with video feeding are immensely high, and the scene context is out of human control, posing an impediment to a successful model of VODT. The presented work has discussed about effectiveness of existing VODT approaches considering its identified taxonomies viz. satellite based, remote sensing-based, unmanned-based, Real-time Tracking based, behavioral analysis and event detection based, integration of multiple data sources, and privacy and ethics. Further, research trend associated with cumulative publications and evolving methods to realize the frequently used methodologies in VODT.Further, the results of review showcase that there is prominent research gap of manifold attributes that demands to be addressed for improving performance of VODT.

show abstract

Section: Miscellaneous Approachesmentioning

confidence: 99%

Section: Research Trend Analysismentioning

confidence: 99%

Methodological Insights Towards Leveraging Performance in Video Object Tracking and Detection

Kurian

2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…Current digital multimedia forensic techniques can be divided into two main categories: active forensics and passive forensics. Active forensics refers to the use of information hiding technology, which embeds imperceptible verification information, such as digital watermark, digital signature, and perceptual hashing, in the process of image or video generation in advance [15]. The party receiving the image or video determines the reliability of the received data by analyzing whether this embedded information has been corrupted.…”

Section: Big Data Processing Analysis Of Athletics' Video Imagesmentioning

confidence: 99%

A Study on the Optimization Simulation of Big Data Video Image Keyframes in Motion Models

Guo

Sun

Tsai

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

In this paper, the signal of athletic sports video image frames is processed and studied according to the technology of big data. The sports video image-multiprocessing technology achieves interference-free research and analysis of sports technology and can meet multiple visual needs of sports technology analysis and evaluation through key technologies such as split-screen synchronous comparison, superimposed synchronous comparison, and video trajectory tracking. The sports video image-processing technology realizes the rapid extraction of key technical parameters of the sports scene, the panoramic map technology of sports video images, the split-lane calibration technology, and the development of special video image analysis software that is innovative in the field of athletics research. An image-blending approach is proposed to alleviate the problem of simple and complex background data imbalance, while enhancing the generalization ability of the network trained using small-scale datasets. Local detail features of the target are introduced in the online-tracking process by an efficient block-filter network. Moreover, online hard-sample learning is utilized to avoid the interference of similar objects to the tracker, thus improving the overall tracking performance. For the feature extraction problem of fuzzy videos, this paper proposes a fuzzy kernel extraction scheme based on the low-rank theory. The scheme fuses multiple fuzzy kernels of keyframe images by low-rank decomposition and then deblurs the video. Next, a double-detection mechanism is used to detect tampering points on the blurred video frames. Finally, the video-tampering points are located, and the specific way of video tampering is determined. Experiments on two public video databases and self-recorded videos show that the method is robust in fuzzy video forgery detection, and the efficiency of fuzzy video detection is improved compared to traditional video forgery detection methods.

show abstract

Computational Contextual Framework for Dynamic Tracking of Numerous Mobile Object

Kumar¹,

Kavya²

2023

Data Science and Algorithms in Systems

View full text Add to dashboard Cite

An Adaptive Video Acquisition Scheme for Object Tracking and Its Performance Optimization

Cited by 6 publications

References 67 publications

Methodological Insights Towards Leveraging Performance in Video Object Tracking and Detection

Methodological Insights Towards Leveraging Performance in Video Object Tracking and Detection

A Study on the Optimization Simulation of Big Data Video Image Keyframes in Motion Models

Computational Contextual Framework for Dynamic Tracking of Numerous Mobile Object

Contact Info

Product

Resources

About