Hybrid approach of video indexing and machine learning for rapid indexing and highly precise object recognition

Tsutsumi, Fujio; Nakajima, Chikahito

doi:10.1109/icip.2001.958576

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…[39] [45] and their group worked on the Video Object Segmentation using the concepts of Spatially Accurate and Temporally Dense Extraction of the Primary Object Regions present in the video streams in an excellent transactions paper. In [46], Tsutsumi et.al. proposed an hybrid approach of video indexing & machine learning concepts for the rapid indexing and highly precise recognition of objects in video streaming.…”

Section: Gu Mei Hua Wumentioning

confidence: 99%

A review of the Multiple Object Detection, Tracking and Recognition in Video Surveillance Systems using different approaches

Patil¹,

N²

2019

Res. Bus.

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Moving item location in any video scene and its detection are the 2 significant testing errands in the moving video investigation system which is dynamic in nature. It is trying because of impediment, nearness of shadows, jumbling, dynamic foundation, clamor etc. Detection of moving objects, following, object coordinating crosswise over multi-camera, and reidentification are the fundamental strides of multi camera video investigation system. Multiple object location and following in savvy city video investigation can be created by propriety of society, for example, shrewd observation, savvy stopping, traffic observing, vehicle route, savvy human services and so forth. The objective of this paper is to examine and audit different methodologies towards such different types of moving object detection, its segmentation, histogram equalization, filtering, classification etc… Also, this survey paper gives a brief idea about the work done by various authors till date in the field of video image processing so that this review papers serves as a base paper for many of the researchers who want to do research in this exciting field of video processing. Some of the drawbacks or the disadvantages of the works that were carried out by the earlier researchers are considered in our research work, studied in brief & problem is being defined along with the objectives in order to overcome some of the deficiencies of the existing algos.

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Section: Gu Mei Hua Wumentioning

confidence: 99%

A review of the Multiple Object Detection, Tracking and Recognition in Video Surveillance Systems using different approaches

Patil¹,

N²

2019

Res. Bus.

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show abstract

“…However, the method is not effective enough when objects are colorful and complex. There are other results [7][8][9] mainly focused on the recognizing and tracking of video objects, but fewer on the retrieval.…”

Section: Introductionmentioning

confidence: 99%

Video Object Retrieval Based on Color Feature Modeling

Huang

Cai

et al. 2010

2010 International Conference on Machine Vision and Human-Machine Interface

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Retrieve of the objects in the videos is a challenging and important work. Non-textual object information stored in videos is presented as grids of numbers in the image flames. Therefore, it is hard to retrieve the object in the videos with classical methods. In this paper, a robust color-feature model of the moving video objects is proposed by converting the RGB pixels to a color circle of hue. Furthermore, we give a framework of video object retrieval based on the color feature model. Finally, the experimental results indicate that the proposed model is accurate and similar to human recognition of the moving objects in videos view, which demonstrates the good performance of the color-feature model.

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“…These methodologies include linear classifiers [31], decision tree [34], neural networks [15], support vector machines [15,30], Bayesian networks [20], factor graphs [21], and hidden Markov models (HMMs) [6,9,22,28,33]. The mapping schemes employed either classify video shots into events or categorize spatio-temporal objects to specific semantic concepts.…”

Section: Introductionmentioning

confidence: 99%

Object-based analysis and interpretation of human motion in sports video sequences by dynamic bayesian networks

Luo

Hwang

2003

Computer Vision and Image Understanding

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Hybrid approach of video indexing and machine learning for rapid indexing and highly precise object recognition

Cited by 7 publications

References 8 publications

A review of the Multiple Object Detection, Tracking and Recognition in Video Surveillance Systems using different approaches

A review of the Multiple Object Detection, Tracking and Recognition in Video Surveillance Systems using different approaches

Video Object Retrieval Based on Color Feature Modeling

Object-based analysis and interpretation of human motion in sports video sequences by dynamic bayesian networks

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