scholarly journals Object Detection and Tracking using Multiple Features Extraction

2021 ◽  
Vol 10 (10) ◽  
pp. 75-79
Author(s):  
Bhavya Rudraiah* ◽  
◽  
Dr. Geetha K. S. ◽  
Keyword(s):  
Feature Extraction ◽  
Object Detection ◽  
Extraction Methods ◽  
Vital Role ◽  
Histogram Of Oriented Gradients ◽  
Multiple Objects ◽  
Multiple Features ◽  
Object Detection And Tracking ◽  
Detection And Tracking ◽  
And Performance

In most of the video analysis applications, object detection and tracking play vital role. Most of detection and tracking algorithms fail to predict multiple objects with varying orientation. In this paper, the goal is to identify and track multiple objects using different feature extraction methods like Locality Sensitive Histogram, Histogram of Oriented Gradients and Edges. These features are subjected to train classifier that can detect the object of different orientations. Experimental results and performance evaluation depicts the proposed method which uses LSH performs well with an increased accuracy of 98%. This method can precisely track the object and can be utilized to track under different scale and pose variations.

scholarly journals Object Detection and Tracking using Faster R-CNN

2019 ◽  
Vol 8 (3) ◽  
pp. 4894-4900
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Batch Size ◽  
Multiple Objects ◽  
Backbone Networks ◽  
Object Detection And Tracking ◽  
Detection And Tracking ◽  
Size Number ◽  
Computer Vision Applications ◽  
Deep Learning Model

This paper uses a deep learning model called Faster R-CNN to detect and track objects in images. Two backbone networks such as ResNet-101 and VGG-16 are tested on a self-created dataset and PASCAL VOC dataset. Intersection over union (IoU) technique is used for the purpose of object tracking. The impacts of batch size, number of iterations and learning rate are analysed. The paper finds that ResNet-101 outperforms VGG-16 significantly by 13% on test data. This finding reinforces that deeper network is better in feature extractions and generalizations. IoU is able to track multiple objects and can identify the loss of track. The processing of frames per second is found to be 5 fps. The study has implications for many computer vision applications. For example, the deep learning based object detection and tracking can either augment the capability of LiDARs and Sensors or become an alternative to them in self-driving vehicles.

scholarly journals The INUS Platform: A Modular Solution for Object Detection and Tracking from UAVs and Terrestrial Surveillance Assets

Computation ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 12
Author(s):  
Evangelos Maltezos ◽  
Athanasios Douklias ◽  
Aris Dadoukis ◽  
Fay Misichroni ◽  
Lazaros Karagiannidis ◽  
...  
Keyword(s):  
Decision Making ◽  
Object Detection ◽  
Situational Awareness ◽  
Machine Learning Techniques ◽  
Measurement Instruments ◽  
Time Data ◽  
Modular Architecture ◽  
Seamless Integration ◽  
Object Detection And Tracking ◽  
Detection And Tracking

Situational awareness is a critical aspect of the decision-making process in emergency response and civil protection and requires the availability of up-to-date information on the current situation. In this context, the related research should not only encompass developing innovative single solutions for (real-time) data collection, but also on the aspect of transforming data into information so that the latter can be considered as a basis for action and decision making. Unmanned systems (UxV) as data acquisition platforms and autonomous or semi-autonomous measurement instruments have become attractive for many applications in emergency operations. This paper proposes a multipurpose situational awareness platform by exploiting advanced on-board processing capabilities and efficient computer vision, image processing, and machine learning techniques. The main pillars of the proposed platform are: (1) a modular architecture that exploits unmanned aerial vehicle (UAV) and terrestrial assets; (2) deployment of on-board data capturing and processing; (3) provision of geolocalized object detection and tracking events; and (4) a user-friendly operational interface for standalone deployment and seamless integration with external systems. Experimental results are provided using RGB and thermal video datasets and applying novel object detection and tracking algorithms. The results show the utility and the potential of the proposed platform, and future directions for extension and optimization are presented.

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