A Survey on Real Time Object Detection, Tracking and Recognition in Image Processing

In recent times face tracking and face recognition have turned out to be increasingly dynamic research field in image processing. This work proposed the framework DEtecting Contiguous Outliers in the LOw-rank Representation for face tracking, in this algorithm the background is assessed by a low-rank network and foreground articles can be distinguished as anomalies. This is suitable for non-rigid foreground motion and moving camera. The face of a foreground person is caught from the frame and then it is contrasted and the speculated pictures stored in the dataset. Here we used Viola-Jones algorithm for face recognition. This approach outperforms the traditional algorithms on multimodal video methodologies and it works adequately on extensive variety of security and surveillance purposes. Results on the continuous demonstrate that the proposed calculation can correctly obtain facial features points. The algorithm is relegate on the continuous camera input and under ongoing ecological conditions.

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Road Object Detection: A Comparative Study of Deep Learning-Based Algorithms

Electronics ◽

10.3390/electronics10161932 ◽

2021 ◽

Vol 10 (16) ◽

pp. 1932

Author(s):

Malik Haris ◽

Adam Glowacz

Keyword(s):

Image Processing ◽

Deep Learning ◽

Object Detection ◽

Real Time ◽

Large Scale ◽

Single Shot ◽

Automated Driving ◽

Convolutional Network ◽

Image Processing Algorithms ◽

Processing Algorithms

Automated driving and vehicle safety systems need object detection. It is important that object detection be accurate overall and robust to weather and environmental conditions and run in real-time. As a consequence of this approach, they require image processing algorithms to inspect the contents of images. This article compares the accuracy of five major image processing algorithms: Region-based Fully Convolutional Network (R-FCN), Mask Region-based Convolutional Neural Networks (Mask R-CNN), Single Shot Multi-Box Detector (SSD), RetinaNet, and You Only Look Once v4 (YOLOv4). In this comparative analysis, we used a large-scale Berkeley Deep Drive (BDD100K) dataset. Their strengths and limitations are analyzed based on parameters such as accuracy (with/without occlusion and truncation), computation time, precision-recall curve. The comparison is given in this article helpful in understanding the pros and cons of standard deep learning-based algorithms while operating under real-time deployment restrictions. We conclude that the YOLOv4 outperforms accurately in detecting difficult road target objects under complex road scenarios and weather conditions in an identical testing environment.

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3D finger tracking and recognition image processing for real-time music playing with depth sensors

Multimedia Tools and Applications ◽

10.1007/s11042-017-4784-9 ◽

2017 ◽

Vol 77 (8) ◽

pp. 9233-9248 ◽

Cited By ~ 6

Author(s):

Enkhtogtokh Togootogtokh ◽

Timothy K. Shih ◽

W. G. C. W. Kumara ◽

Shih-Jung Wu ◽

Shih-Wei Sun ◽

...

Keyword(s):

Image Processing ◽

Real Time ◽

Depth Sensors ◽

Finger Tracking ◽

Tracking And Recognition

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Role of Clustering Techniques in Effective Image Segmentation

Advancements in Computer Vision and Image Processing - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-5225-5628-2.ch006 ◽

2018 ◽

pp. 128-160

Author(s):

Bhavneet Kaur ◽

Meenakshi Sharma

Keyword(s):

Image Processing ◽

Computer Vision ◽

Image Segmentation ◽

Object Detection ◽

Real Time ◽

Vision Research ◽

Medical Equipment ◽

Additional Advantage ◽

Clustering Techniques

Image segmentation is gauged as an essential stage of representation in image processing. This process segregates a digitized image into various categorized sections. An additional advantage of distinguishing dissimilar objects can be represented within this state of the art. Numerous image segmentation techniques have been proposed by various researchers, which maintained a smooth and easy timely evaluation. In this chapter, an introduction to image processing along with segmentation techniques, computer vision fundamentals, and its applied applications that will be of worth to the image processing and computer vision research communities has been deeply studied. It aims to interpret the role of various clustering-based image segmentation techniques specifically. Use of the proposed chapter if made in real time can project better outcomes in object detection and recognition, which can then later be applied in numerous applications and devices like in robots, automation, medical equipment, etc. for safety, advancement, and betterment of society.

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Object Detection and Identification in Surveillance Images using Image Processing

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a1057.1291s319 ◽

2019 ◽

Vol 9 (1S3) ◽

pp. 300-305

Keyword(s):

Image Processing ◽

Object Detection ◽

Object Tracking ◽

Range Size ◽

Its Sequence ◽

Surveillance Camera ◽

Color Range ◽

Detection And Identification ◽

Illumination Changes ◽

Tracking And Recognition

The goal of object detection and identification in surveillance images using image processing is to detect a particular part of the image from surveillance camera like an object’s position, movement, and its sequence. Object tracking and recognition deal with recognizing the image of video which can differ in color, range, size, illumination changes with time and some cluttered images. As this paper has been surveying and an algorithm has been proposed and implemented, the identified object has freed from the shadow, clutter, illumination changes were detected and eliminated appropriately.

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Electronic Eye for Blind People with Object Detection and Audio Output Using Image Processing

International Journal of Advanced Research in Science, Communication and Technology ◽

10.48175/ijarsct-1043 ◽

2021 ◽

pp. 403-406

Author(s):

Atharva Shewale ◽

Mrunalini Mahakalkar ◽

Vijay Pawar ◽

Yajan Bharad ◽

Dr. Shwetambari Chiwhane

Keyword(s):

Machine Learning ◽

Image Processing ◽

Object Detection ◽

Real Time ◽

Blind People ◽

Blind Person ◽

Great Problem ◽

Unknown Environment ◽

Audio Output ◽

Electronic Eye

One of the major issues faced by Blind people is detecting and recognizing an object. The objective of this project is to help the blind people because mobility of blind people is always a great problem. The mobility of blind people in unknown environment seems impossible without external help, because they don’t have any proper idea about their surroundings. So, we are developing a electronic eye which helps them to know about their surroundings and also guide them during travelling. Developing a system based on image processing using DNN algorithm which is able to labeling objects with the help of OpenCV and Tensor flow libraries and converting the labeled text in to speech and producing output in the form of audio to make the blind person aware of the object in front of him or her. The scope of this system is also measuring the distance of the object from the person and reporting the same Object detection using image processing and Machine Learning. It searches the object. We want to innovate our system the possibility of using the hearing sense to understand real time objects. For the security purpose track blind people in real time environment.

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