Real-time object detection and tracking

Naeem, Hammad; Ahmad, Jawad; Tayyab, Muhammad

doi:10.1109/inmic.2013.6731341

Cited by 17 publications

(4 citation statements)

References 14 publications

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“…In relation to real-time processing, a paper from HITEC university recommends how to approach this issue [ 22 ]. Using only software solutions for complex object tracking is not viable due to limitations in processing capabilities.…”

Section: Discussionmentioning

confidence: 99%

Object detection and tracking using a high-performance artificial intelligence-based 3D depth camera: towards early detection of African swine fever

Ryu

Tai

2022

J Vet Sci

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Background Inspection of livestock farms using surveillance cameras is emerging as a means of early detection of transboundary animal disease such as African swine fever (ASF). Object tracking, a developing technology derived from object detection aims to the consistent identification of individual objects in farms. Objectives This study was conducted as a preliminary investigation for practical application to livestock farms. With the use of a high-performance artificial intelligence (AI)-based 3D depth camera, the aim is to establish a pathway for utilizing AI models to perform advanced object tracking. Methods Multiple crossovers by two humans will be simulated to investigate the potential of object tracking. Inspection of consistent identification will be the evidence of object tracking after crossing over. Two AI models, a fast model and an accurate model, were tested and compared with regard to their object tracking performance in 3D. Finally, the recording of pig pen was also processed with aforementioned AI model to test the possibility of 3D object detection. Results Both AI successfully processed and provided a 3D bounding box, identification number, and distance away from camera for each individual human. The accurate detection model had better evidence than the fast detection model on 3D object tracking and showed the potential application onto pigs as a livestock. Conclusions Preparing a custom dataset to train AI models in an appropriate farm is required for proper 3D object detection to operate object tracking for pigs at an ideal level. This will allow the farm to smoothly transit traditional methods to ASF-preventing precision livestock farming.

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Section: Discussionmentioning

confidence: 99%

Object detection and tracking using a high-performance artificial intelligence-based 3D depth camera: towards early detection of African swine fever

Ryu

Tai

2022

J Vet Sci

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“…This allows for rich semantics extraction at all levels and enables end-to-end training without sacrificing speed or memory efficiency. FPN is versatile and can be applied to various computer vision tasks like instance segmentation and region proposal creation [2].The process of object detection involves eliminating irrelevant noise from input data, such as images or videos, to train models effectively. Convolutional neural networks (CNNs) serve as the backbone of object detection models, handling each significant component intricately.…”

Section: IImentioning

confidence: 99%

Live Object Recognition Using YOLO

Sonawane,

Gudur,

Gaikwad

et al. 2024

IJRASET

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Live object recognition refers to the real-time process of identifying and categorizing objects within a given visual input, such as images. This technology utilizes computer vision techniques and advanced algorithms to detect objects, determine their dimension, area and weight and often classify them into defined categories. Our system proposesR-CNN and YOLO to determine the dimensions of the objects in real time. YOLO takes a different approach by treating object detection as a single regression problem. A single neural network is trained to directly predict bounding boxes and class probabilities for multiple objects in an image. The input image is divided into a grid, and each grid cell is responsible for predicting the objects whose center fall within that cell. Live object recognition finds applications in various fields, including autonomous vehicles, surveillance systems, robotics,augmented reality, and more. By providing instantaneous and accurate insights into the surrounding environment, this technology contributes to enhanced decision-making,interaction, and automation across numerous domains. The objects which can be recognized are solid objects which we use daily such as electronic items, stationery items, culinary items and many more. Our system “Live Object Recognition using YOLO” is aimed to detect and determine the objects and dimensions in real time.

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“…The authors in [14] focus on the spatial audio technique implemented for 3D multimedia content including spatial audio and video. The spatial audio rendering method based on wave field synthesis is particularly useful for applications where multiple listeners experience a true spatial sound while being free to move around without losing spatial sound properties.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Empowering Blind Tennis via Music

Pirani¹,

Kulkarni²,

Ali³

et al. 2018

IJCA

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Lawn Tennis is one of the most popular sports all around the world. Especially, during last few years, a great deal of highend technology is being used to follow every point of every match of the Tennis Tournaments. Unfortunately, visually challenged players cannot take the pleasure of following and playing this sport completely as they face the challenge of determining the position of the ball accurately on the court which increases their stress and anxiety levels and hence numerous players eventually give up. The training period is difficult not only for the visually-impaired players but also the coaches because the players have to be trained rigorously to determine how the ball is being hit, from where it is currently, and where the ball will land on the court. So, there is a high need for a system that makes the sport more enjoyable and less stressful for the visually-impaired people. Moreover, the existing system to play the sport of Blind Tennis makes use of sound balls, special balls that rattle when they bounce. This paper presents a comprehensive survey of Blind Tennis, the existing technologies that analyze the game and propose a system that would enable the visually-impaired people to not only play the sport but also follow the sport with enthusiasm just like general public by following every point of the match closely.

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Real-time object detection and tracking

Cited by 17 publications

References 14 publications

Object detection and tracking using a high-performance artificial intelligence-based 3D depth camera: towards early detection of African swine fever

Object detection and tracking using a high-performance artificial intelligence-based 3D depth camera: towards early detection of African swine fever

Live Object Recognition Using YOLO

Empowering Blind Tennis via Music

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