Object Tracking System Using Approximate Median Filter, Kalman Filter and Dynamic Template Matching

Rao, Guoyao; Satyanarayana, Ch.

doi:10.5815/ijisa.2014.05.09

Cited by 15 publications

(4 citation statements)

References 6 publications

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“…Online video object tracking with the new integrated technique was carried out by Mallikarjuna Rao et al in 2014. In which, the LBP was used along with colour RGB model and Sobel Edge Detector [7]. The joint colour -texture and joint colour -Local Rhombus Pattern (LRP) were proposed for patterning the futures by Pallavi et al in the year of 2015.…”

Section: Related Workmentioning

confidence: 99%

“…There are several methods that are used for mapping the histograms in frame sequences. Amidst Kalman filer estimation with template matching [8], Bayesian tracking method [2], Particle Filtering [7] and Mean Shift keying technique [14][13][7][9] [15] the Mean shift Tracking algorithm is often used due to its efficiency. Several algorithms used this mean shift key algorithm for tracking the objects in video frames.…”

Section: Related Workmentioning

confidence: 99%

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novel object detection technique for videos with homogeneous background

Devakumar

Murugan

2022

ijhs

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Object detection and tracking in video frames play a major role in all the computer vision systems. Several computer vision systems use background subtraction method for detecting the objects. However, Background subtraction method often results in lack of accuracy during frame comparison. Also, it leads to increase in memory requirements and computational complexity while setting threshold. The new Local Mean Diamond Pattern (LMDP) is introduced here to overcome the existing issues by extracting the objects directly from the frames. The extracted objects are tracked using mean shift tracking algorithm. The experimental result shows that the LMDP outperforms the existing methods viz., Local Binary Pattern (LBP), Local Extrema Pattern (LEP) and Local Rhombus Pattern (LRP) in terms of accuracy.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

novel object detection technique for videos with homogeneous background

Devakumar

Murugan

2022

ijhs

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“…Their method works for static camera with moving face(s) video sequences and fails to track multiple faces. Ranganatha S et al [26] have also developed an algorithm for face tracking by integrating improved CAMSHIFT [27,31] and kalman filter [28][29][30]. Their face tracking algorithm work faster and solve the problem of illumination.…”

Section: Related Workmentioning

confidence: 99%

Development of Robust Multiple Face Tracking Algorithm and Novel Performance Evaluation Metrics for Different Background Video Sequences

Ranganatha¹,

Gowramma²

2018

IJISA

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In computer vision, face tracking is having wider opportunities for research activities using different background video sequences because of various factors and constraints. Due to the challenges that are increasing day by day, old/existing algorithms are becoming obsolete. There are many powerful algorithms that are limited to certain set of video sequences. In this paper, we are proposing an algorithm that detect and track multiple faces in different background video sequences. Viola-Jones face detection algorithm is used in such a way that, new face/first face need not to be in the starting frame of the selected video sequence. The proposed algorithm successfully detect new face(s) along with existing face(s) by keeping track of the facial data using BRISK feature points. The mean of the old points and new points are calculated based on the area of the facial data. The detected face(s) in further frames undergoes similarity check with existing facial data. If detected facial data and existing facial data mismatches, then the detected facial data is entered into face tracks structure. By using point tracker method, the proposed algorithm track those points that has been set for each of the facial data.

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“…To search an object manually in huge videos is an exhausting task. Also in video surveillance [9] [10] partial object detection is a challenging task. Objects in the video may be of any dimension, color and may present at any angles, detecting these objects is difficult.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Object Search in Video Using Template Matching

Ujgare¹,

Baviskar²

2019

IJIGSP

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This research paper presents a novel approach for object instance search in video. At the inception, video is selected for which the object instance within the desired video is to be searched and given as an input to system. In preprocessing step, video is divided into key frames. In next step, features are extracted from query image and using template matching algorithm it is compared with key frames. If the object is present in frame then it will display detected object. Similarly, all the frames in video which contains the object are displayed. Max Path Search algorithm is used to remove the noise against classifier and Spatio-Temporal trajectories are used to improve object search. We encountered the fundamental challenge to detect an object from a set of key frames of a video with a partial appearance of object due to lighting, positioning, occlusion etc. from a known class such as logo and any other. The goal of proposed method is to detect all instances of object from known class.

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Object Tracking System Using Approximate Median Filter, Kalman Filter and Dynamic Template Matching

Cited by 15 publications

References 6 publications

novel object detection technique for videos with homogeneous background

novel object detection technique for videos with homogeneous background

Development of Robust Multiple Face Tracking Algorithm and Novel Performance Evaluation Metrics for Different Background Video Sequences

An Efficient Object Search in Video Using Template Matching

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