Real- Time Tracking for Multiple Objects Based on Implementation of RGB Color Space in Video

Wilson, K

doi:10.14257/ijsip.2016.9.4.29

Cited by 3 publications

(4 citation statements)

References 15 publications

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“…Wilson [20] in his paper presents an algorithm for real time multiple objects tracking using RGB color space in video. Preprocessing and thresholding is used to color conversion and identify all the objects in the video respectively.…”

Section: Technology Reviewmentioning

confidence: 99%

Smart Veggie Identification and Alerting System for Supermarkets using Image Processing Techniques and Neural Networks

Hewasinghe¹,

Pemarathne²

2018

IJCA

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The recent advances in the field of image processing has become a powerful quantitative method in development of science and engineering. Image recognition is one of the foremost areas in computer vision, it yields high degree understanding through computers, one of the maximum crucial regions in recognition is object recognition that's the process of locating a specific object in an image or video surveillance. This paper presents the current state of image processing techniques to identify vegetables and fruits and its capabilities to improve hardware resources with the growing demands of the business industry. The proposed system can identify vegetables and fruits in a basket that are to be retailed and alert the authorities when a basket is going to be finished. The application is based on color and size comparison of the vegetables and fruits in a live video with a reference image and there by extract similar features by using neural network for identification. Baskets are marked with a colored line to indicate the level of the item. Particular level is identified through color identification and notify the responsible parties.

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Section: Technology Reviewmentioning

confidence: 99%

Smart Veggie Identification and Alerting System for Supermarkets using Image Processing Techniques and Neural Networks

Hewasinghe¹,

Pemarathne²

2018

IJCA

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“…After completing the context model update task, accelerate the computation speed with fast Fourier transform. Finally, select the target area to track according to the best value of the latest confidence map [17].…”

Section: Confidence Mapmentioning

confidence: 99%

“…The tracking target scale may alter at different times, so add the scale parameter to scale ω σ , fitting the change of scale parameter when σ changes. Specific program updates are shown in Equation (17):…”

Section: Confidence Mapmentioning

confidence: 99%

Target Tracking based on KCF Combining with Spatio-Temporal Context Learning

Wang¹

2018

IJPE

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Most target tracking is based on a lot of samples training to build the model of the target, which is then carried on the tracking processing. This will need to choose a lot of tracked target samples for learning and training. However, there are all kinds of deformation of the training samples, including variety of light and scale, and so on, causing the long computation time, high computational complexity, and less robustness. The traditional kernel correlation filtering (KCF) tracking is through online learning of the first frame in the target vide. It then uses cyclic matrix to strengthen samples robustness, reducing the complexity of the calculation and time. But, the traditional KCF nuclear is unsatisfactory used for complex scenarios and quick treatment. In this paper, under the framework of the KCF, the target context information is introduced to make the tracking have better robustness and a better effect to deal with complex scenarios.

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“…(1) Features: texture and color [13][14][15] (2) Combining methods: combining two or more background models as the new model [16] (3) Updating the background model [17] In this paper, a new pixelwise and nonparametric moving object detection method is proposed. Background model is built by the first 1 frames and sampling times in 3 × 3 neighborhood region randomly.…”

Section: Introductionmentioning

confidence: 99%

Moving Object Detection for Dynamic Background Scenes Based on Spatiotemporal Model

Yang

Zhang

Wang

et al. 2017

Advances in Multimedia

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Moving object detection in video streams is the first step of many computer vision applications. Background modeling and subtraction for moving detection is the most common technique for detecting, while how to detect moving objects correctly is still a challenge. Some methods initialize the background model at each pixel in the first N frames. However, it cannot perform well in dynamic background scenes since the background model only contains temporal features. Herein, a novel pixelwise and nonparametric moving object detection method is proposed, which contains both spatial and temporal features. The proposed method can accurately detect the dynamic background. Additionally, several new mechanisms are also proposed to maintain and update the background model. The experimental results based on image sequences in public datasets show that the proposed method provides the robustness and effectiveness in dynamic background scenes compared with the existing methods.

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Real- Time Tracking for Multiple Objects Based on Implementation of RGB Color Space in Video

Abstract: Abstract

Cited by 3 publications

References 15 publications

Smart Veggie Identification and Alerting System for Supermarkets using Image Processing Techniques and Neural Networks

Smart Veggie Identification and Alerting System for Supermarkets using Image Processing Techniques and Neural Networks

Target Tracking based on KCF Combining with Spatio-Temporal Context Learning

Moving Object Detection for Dynamic Background Scenes Based on Spatiotemporal Model

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