Real Time Object Detection and Tracking Using Deep Learning and OpenCV

Chandan, G; Jain, Ayush; Jain, Harsh; Mohana,

doi:10.1109/icirca.2018.8597266

Cited by 195 publications

(72 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Some object detection algorithms include the regionbased convolutional neural networks (RCNN), Faster-RCNN, the single shot detector (SSD) and you only look once (YOLO). Among these, Faster-RCNN and SSD achieve higher accuracy, while YOLO offers is more advantageous speed is given preference over accuracy [191].…”

Section: Deep Learning For Decision Making In Risk Management At mentioning

confidence: 99%

A Deep Learning Approach Towards Railway Safety Risk Assessment

Alawad

Kaewunruen

2020

IEEE Access

View full text Add to dashboard Cite

Railway stations are essential aspects of railway systems, and they play a vital role in public daily life. Various types of AI technology have been utilised in many fields to ensure the safety of people and their assets. In this paper, we propose a novel framework that uses computer vision and pattern recognition to perform risk management in railway systems in which a convolutional neural network (CNN) is applied as a supervised machine learning model to identify risks. However, risk management in railway stations is challenging because stations feature dynamic and complex conditions. Despite extensive efforts by industry associations and researchers to reduce the number of accidents and injuries in this field, such incidents still occur. The proposed model offers a beneficial method for obtaining more accurate motion data, and it detects adverse conditions as soon as possible by capturing fall, slip and trip (FST) events in the stations that represent high-risk outcomes. The framework of the presented method is generalisable to a wide range of locations and to additional types of risks.

show abstract

Section: Deep Learning For Decision Making In Risk Management At mentioning

confidence: 99%

A Deep Learning Approach Towards Railway Safety Risk Assessment

Alawad

Kaewunruen

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Then, fast-tracking with less accuracy. Bochinski et al [16] proposed a new tracking method based on Intersection over Union (IOU) Algorithm. It shows a simple tracker with some drawbacks in multi-object tracking.…”

Section: Related Workmentioning

confidence: 99%

“…7. The anchors or surrounding squares calculated in advance were created and are of a specific size as well as match the original real squares and correspond to them in the distribution [16]. It was also selected so that the cross-to-union ratio called (IOU) was more significant than 0.5.…”

Section: Intersection Over the Union (Iou)mentioning

confidence: 99%

See 1 more Smart Citation

Enhancement Performance of Multiple Objects Detection and Tracking for Real-time and Online Applications

Abdulghafoor¹,

Abdullah²

2020

IJIES

View full text Add to dashboard Cite

Multi-object detection and tracking systems represent one of the basic and important tasks of surveillance and video traffic systems. Recently. The proposed tracking algorithms focused on the detection mechanism. It showed significant improvement in performance in the field of computer vision. Though. It faced many challenges and problems, such as many blockages and segmentation of paths, in addition to the increasing number of identification keys and false-positive paths. In this work, an algorithm was proposed that integrates information on appearance and visibility features to improve the tracker's performance. It enables us to track multiple objects throughout the video and for a longer period of clogging and buffer a number of ID switches. An effective and accurate data set, tools, and metrics were also used to measure the efficiency of the proposed algorithm. The experimental results show the great improvement in the performance of the tracker, with high accuracy of more than 65%, which achieves competitive performance with the existing algorithms.

show abstract

“…First step in training a network using deep learning for an application is to prepare an appropriate dataset and make Train-Test Split depending on the available data. Suitable network is designed or selected (in case of Transfer Learning) for training [13]. Validation Loss is monitored throughout the training process to produce a very less constant value after few epochs, if not then the hyper parameter tuning is performed on model to give lowest possible validation loss values.…”

Section: A Neural Network Trainingmentioning

confidence: 99%

Object Detection and Tracking using Deep Learning and Artificial Intelligence for Video Surveillance Applications

Mohana¹,

Aradhya²

2019

IJACSA

View full text Add to dashboard Cite

Data is the new oil in current technological society. The impact of efficient data has changed benchmarks of performance in terms of speed and accuracy. The enhancement is visualizable because the processing of data is performed by two buzzwords in industry called Computer Vision (CV) and Artificial Intelligence (AI). Two technologies have empowered major tasks such as object detection and tracking for traffic vigilance systems. As the features in image increases demand for efficient algorithm to excavate hidden features increases. Convolution Neural Network (CNN) model is designed for urban vehicle dataset for single object detection and YOLOv3 for multiple object detection on KITTI and COCO dataset. Model performance is analyzed, evaluated and tabulated using performance metrics such as True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN), Accuracy, Precision, confusion matrix and mean Average Precession (mAP). Objects are tracked across the frames using YOLOv3 and Simple Online Real Time Tracking (SORT) on traffic surveillance video. This paper upholds the uniqueness of the state of the art networks like DarkNet. The efficient detection and tracking on urban vehicle dataset is witnessed. The algorithms give real-time, accurate, precise identifications suitable for realtime traffic applications.

show abstract

Real Time Object Detection and Tracking Using Deep Learning and OpenCV

Cited by 195 publications

References 2 publications

A Deep Learning Approach Towards Railway Safety Risk Assessment

A Deep Learning Approach Towards Railway Safety Risk Assessment

Enhancement Performance of Multiple Objects Detection and Tracking for Real-time and Online Applications

Object Detection and Tracking using Deep Learning and Artificial Intelligence for Video Surveillance Applications

Contact Info

Product

Resources

About