Overview of Object Detection and Tracking based on Block Matching Techniques

Samdurkar, Apurva S.; Kamble, Shailesh D.; Thakur, Nileshsingh V.; Patharkar, Akshay S.

doi:10.15439/2017r84

Cited by 3 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Naive Bayes classifier a probabilistic approach that is simple but effective for face recognition problems [1]. This classifier has been used in various applications like image recognition [12], image retrieval [11] and natural language processing. The EM algorithm (Expectation Maximization algorithm) is a structure which determines probabilistic parameter, when the data has missing values.…”

Section: Fig2: Face Recognition Process [2]mentioning

confidence: 99%

Face Recognition using Probabilistic Model for Locally Changed Face

Khedgaonkar¹,

Singh²,

Raghuwanshi³

et al. 2019

IJITEE

View full text Add to dashboard Cite

Face recognition is an attention-grabbing area in research field due to various challenges like aging, pose variation, facial expression, and illumination problem. Now-a-days, plastic surgery is a standout amongst the above mentioned exciting issues of face recognition. Local plastic surgery is a type of plastic surgery in which any one feature of the face is changed instead of all features of face. In this paper, the face recognition on local plastic surgical faces using probabilistic approach is presented, where a probabilistic approach like Naive Bayes Classifier, Neural Network Classifier are used to recognize the faces with local plastic surgery from the database. Naive Bayes classifier is fused with Expectation Maximization Algorithm (EMA) for better recognition of the faces from the database. Finally, Results of Naive Bayes Classifier, Naive Bayes Classifier with EMA is evaluated on standard Plastic Surgery Database(PSD). Similarly, Neural network classifier is also been tested on PSD database, which will aid to decide which classifier is efficient for recognizing plastic surgical faces. The motive of this paper is give increase in recognition rate with the help of effective classifier.

show abstract

Section: Fig2: Face Recognition Process [2]mentioning

confidence: 99%

Face Recognition using Probabilistic Model for Locally Changed Face

Khedgaonkar¹,

Singh²,

Raghuwanshi³

et al. 2019

IJITEE

View full text Add to dashboard Cite

show abstract

“…A camera network consists of connected single cameras that expand the sensing coverage of the environment. Networked cameras are usually connected in two ways [26,35]. The first way is through a technique called overlapping, in which cameras are closely placed together such that the images produced by neighboring cameras overlap with a certain portion.…”

Section: Integration Of Multiple Sensing Systemsmentioning

confidence: 99%

A Survey of Hardware Advances and Techniques for Vision-Based Object Detection, Classification, and Tracking

Selje¹,

Sun²

2018

International Conference of Control, Dynamic Systems, and Robotics

View full text Add to dashboard Cite

Mobile object tracking is a significant and challenging task in computer vision that plays a vital role in various applications, such as artificial intelligence, autonomous vehicles, medical imaging, robotics, and surveillance systems. The major contribution of this review paper includes the discussion of various hardware platforms used to acquire raw imagery information and implement computer vision algorithms in the phases of object detection, object recognition and classification, and object tracking.

show abstract

“…Video files can be seen as a 2-D image through time (Khobahi, 2017). Video analysis is a complicated problem, and it can be summarized in three basic steps as follows (Samdurkar et al, 2017):…”

Section: Introductionmentioning

confidence: 99%

Improved Real Time Target Tracking System Based on Cam-shift and Kalman Filtering Techniques

Fiyad,

Abozied,

Metwally

et al. 2023

JART

View full text Add to dashboard Cite

This paper presents an improved real time target tracking system based on Cam-Shift and advanced Kalman filter algorithms. The paper, proposes a simple low implementation cost scheme while keeping the high tracking performance. Two degrees of freedom target tracking system based on two hybrid stepper motors with their drive systems is fully designed and implemented to improve the tracking accuracy while keeping low-cost. The potential of the proposed algorithm lies in its ability to accurately detect, track and produce firing commands on the designated target while utilizing low cost on the shelf embedded Microcontroller boards. A low cost Universal Serial Port (USB) camera is used and integrated with the proposed algorithm in order to detect and track maneuvering targets. A series of experiments for stationary and moving objects are carried out for testing the proposed system efficiency. The proposed system enables accurate performance for tracking static and moving objects. Hence it provides a simple and low implementation cost scheme for real time object tracking applications.

show abstract

Overview of Object Detection and Tracking based on Block Matching Techniques

Cited by 3 publications

References 18 publications

Face Recognition using Probabilistic Model for Locally Changed Face

Face Recognition using Probabilistic Model for Locally Changed Face

A Survey of Hardware Advances and Techniques for Vision-Based Object Detection, Classification, and Tracking

Improved Real Time Target Tracking System Based on Cam-shift and Kalman Filtering Techniques

Contact Info

Product

Resources

About