scholarly journals Methods and Algorithms for Detecting Objects in Video Files

2018 ◽  
Vol 155 ◽  
pp. 01016 ◽  
Author(s):  
Cuong Nguyen The ◽  
Dmitry Shashev
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Traffic Control ◽  
Motion Pictures ◽  
Real Life ◽  
Video File ◽  
Detection And Tracking ◽  
Object Movement ◽  
Wide Range ◽  
Automated Processing

Video files are files that store motion pictures and sounds like in real life. In today's world, the need for automated processing of information in video files is increasing. Automated processing of information has a wide range of application including office/home surveillance cameras, traffic control, sports applications, remote object detection, and others. In particular, detection and tracking of object movement in video file plays an important role. This article describes the methods of detecting objects in video files. Today, this problem in the field of computer vision is being studied worldwide.

Multi Object Detection and Tracking from Video File

2014 ◽  
Vol 533 ◽  
pp. 218-225 ◽  
Author(s):  
Rapee Krerngkamjornkit ◽  
Milan Simic
Keyword(s):  
Computer Vision ◽  
Background Subtraction ◽  
Moving Objects ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Video Tracking ◽  
Partial Occlusion ◽  
Video File ◽  
Detection And Tracking ◽  
Over Time

This paper describes computer vision algorithms for detection, identification, and tracking of moving objects in a video file. The problem of multiple object tracking can be divided into two parts; detecting moving objects in each frame and associating the detections corresponding to the same object over time. The detection of moving objects uses a background subtraction algorithm based on Gaussian mixture models. The motion of each track is estimated by a Kalman filter. The video tracking algorithm was successfully tested using the BIWI walking pedestrians datasets [. The experimental results show that system can operate in real time and successfully detect, track and identify multiple targets in the presence of partial occlusion.

scholarly journals Object Detectors’ Convolutional Neural Networks backbones : a review and a comparative study

2021 ◽  
Vol 9 (11) ◽  
pp. 1379-1386
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Object Detection ◽  
Convolutional Neural Networks ◽  
Crucial Role ◽  
Extended Version ◽  
Backbone Networks ◽  
Detection Algorithms ◽  
Wide Range

Computer vision is a scientific field that deals with how computers can acquire significant level comprehension from computerized images or videos. One of the keystones of computer vision is object detection that aims to identify relevant features from video or image to detect objects. Backbone is the first stage in object detection algorithms that play a crucial role in object detection. Object detectors are usually provided with backbone networks designed for image classification. Object detection performance is highly based on features extracted by backbones, for instance, by simply replacing a backbone with its extended version, a large accuracy metric grows up. Additionally, the backbone's importance is demonstrated by its efficiency in real-time object detection. In this paper, we aim to accumulate the crucial role of the deep learning era and convolutional neural networks in particular in object detection tasks. We have analyzed and have been concentrating on a wide range of reviews on convolutional neural networks used as the backbone of object detection models. Building, therefore, a review of backbones that help researchers and scientists to use it as a guideline for their works.

scholarly journals A Performance Comparison and Enhancement of Animal Species Detection in Images with Various R-CNN Models

AI ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 552-577
Author(s):  
Mai Ibraheam ◽  
Kin Fun Li ◽  
Fayez Gebali ◽  
Leonard E. Sielecki
Keyword(s):  
Object Detection ◽  
Network Architecture ◽  
Animal Species ◽  
Detection System ◽  
Real Life ◽  
Medical Diagnostics ◽  
Performance Comparison ◽  
Species Detection ◽  
Detection Techniques ◽  
Wide Range

Object detection is one of the vital and challenging tasks of computer vision. It supports a wide range of applications in real life, such as surveillance, shipping, and medical diagnostics. Object detection techniques aim to detect objects of certain target classes in a given image and assign each object to a corresponding class label. These techniques proceed differently in network architecture, training strategy and optimization function. In this paper, we focus on animal species detection as an initial step to mitigate the negative impacts of wildlife–human and wildlife–vehicle encounters in remote wilderness regions and on highways. Our goal is to provide a summary of object detection techniques based on R-CNN models, and to enhance the performance of detecting animal species in accuracy and speed, by using four different R-CNN models and a deformable convolutional neural network. Each model is applied on three wildlife datasets, results are compared and analyzed by using four evaluation metrics. Based on the evaluation, an animal species detection system is proposed.

scholarly journals Object Detection and Movement Tracking Using Tubelets and Faster RCNN Algorithm with Anchor Generation

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Prabu Mohandas ◽  
Jerline Sheebha Anni ◽  
Rajkumar Thanasekaran ◽  
Khairunnisa Hasikin ◽  
Muhammad Mokhzaini Azizan
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Tracking System ◽  
Detection And Tracking ◽  
Occluded Objects ◽  
Object Movement ◽  
Localization Errors ◽  
Crop Damages ◽  
Improved Performance ◽  
Movement Tracking

Object detection in images and videos has become an important task in computer vision. It has been a challenging task due to misclassification and localization errors. The proposed approach explored the feasibility of automated detection and tracking of elephant intrusion along forest border areas. Due to an alarming increase in crop damages resulted from movements of elephant herds, combined with high risk of elephant extinction due to human activities, this paper looked into an efficient solution through elephant’s tracking. The convolutional neural network with transfer learning is used as the model for object classification and feature extraction. A new tracking system using automated tubelet generation and anchor generation methods in combination with faster RCNN was developed and tested on 5,482 video sequences. Real-time video taken for analysis consisted of heavily occluded objects such as trees and animals. Tubelet generated from each video sequence with intersection over union (IoU) thresholds have been effective in tracking the elephant object movement in the forest areas. The proposed work has been compared with other state-of-the-art techniques, namely, faster RCNN, YOLO v3, and HyperNet. Experimental results on the real-time dataset show that the proposed work achieves an improved performance of 73.9% in detecting and tracking of objects, which outperformed the existing approaches.

scholarly journals Camouflaged Object Detection and Tracking: A Survey

2020 ◽  
Vol 20 (04) ◽  
pp. 2050028
Author(s):  
Ajoy Mondal
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Research Direction ◽  
Point Of View ◽  
Future Research ◽  
Theoretical Point ◽  
Future Research Direction ◽  
Object Detection And Tracking ◽  
Detection And Tracking ◽  
Survey Papers

Moving object detection and tracking have various applications, including surveillance, anomaly detection, vehicle navigation, etc. The literature on object detection and tracking is rich enough, and there exist several essential survey papers. However, the research on camouflage object detection and tracking is limited due to the complexity of the problem. Existing work on this problem has been done based on either biological characteristics of the camouflaged objects or computer vision techniques. In this paper, we review the existing camouflaged object detection and tracking techniques using computer vision algorithms from the theoretical point of view. This paper also addresses several issues of interest as well as future research direction in this area. We hope this paper will help the reader to learn the recent advances in camouflaged object detection and tracking.

scholarly journals DeepSTN+: Context-Aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis

2019 ◽  
Vol 33 ◽  
pp. 1020-1027 ◽  
Author(s):  
Ziqian Lin ◽  
Jie Feng ◽  
Ziyang Lu ◽  
Yong Li ◽  
Depeng Jin
Keyword(s):  
Traffic Control ◽  
State Of The Art ◽  
Real Life ◽  
Time Interval ◽  
Context Aware ◽  
Training Process ◽  
Flow Prediction ◽  
Wide Range ◽  
The City ◽  
Historical Flow

Crowd flow prediction is of great importance in a wide range of applications from urban planning, traffic control to public safety. It aims to predict the inflow (the traffic of crowds entering a region in a given time interval) and outflow (the traffic of crowds leaving a region for other places) of each region in the city with knowing the historical flow data. In this paper, we propose DeepSTN+, a deep learning-based convolutional model, to predict crowd flows in the metropolis. First, DeepSTN+ employs the ConvPlus structure to model the longrange spatial dependence among crowd flows in different regions. Further, PoI distributions and time factor are combined to express the effect of location attributes to introduce prior knowledge of the crowd movements. Finally, we propose an effective fusion mechanism to stabilize the training process, which further improves the performance. Extensive experimental results based on two real-life datasets demonstrate the superiority of our model, i.e., DeepSTN+ reduces the error of the crowd flow prediction by approximately 8%∼13% compared with the state-of-the-art baselines.

Omni Directional Moving Object Detection and Tracking With Virtual Reality Feedback

2017 ◽  
Author(s):  
Armaan Zirakchi ◽  
Cody Lee Lundberg ◽  
Hakki Erhan Sevil
Keyword(s):  
Computer Vision ◽  
Virtual Reality ◽  
Object Detection ◽  
Motion Detection ◽  
Situational Awareness ◽  
Field Of View ◽  
Current Frame ◽  
Detection And Tracking ◽  
Camera View ◽  
Vehicle Systems

Computer vision methods are commonly used to detect and track motion using conventional cameras, however, that is limited with the field of view (FOV) of the camera. This study is to attempt to overcome this challenge by using a 360 degree camera. Our approach utilizes background subtracter from OpenCV Library which creates a continuously updating background model for the motion detection. The model is subtracted from the current frame leaving contours symbolizing the movement observed in the camera view. These contours are then analyzed and processed so that the system can track the largest contour. The tracked movement is outlined and directed to the user via Virtual Reality (VR) headset. The VR headset only displays a 60 degree portion of the camera view to the user which provides more realistic situational awareness of the surroundings for the user. These activities are a part of a larger effort to establish a foundation for autonomous unmanned vehicle systems with situational awareness capabilities.

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