Background Subtraction Using an Adaptive Local Median Texture Feature in Illumination Changes Urban Traffic Scenes

Zhang, Yunsheng; Zheng, Weibo; Leng, Kaijun; Li, Hao

doi:10.1109/access.2020.3009104

Cited by 19 publications

(10 citation statements)

References 41 publications

(51 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e full convolutional network converts single label retrieval into multilabel retrieval [32], and selecting the proper sample pattern is critical for reconstructing highquality pictures [33]. A sampling strategy based on a probability mass function may dynamically modify the sample rate depending on data acquired in advance.…”

Section: Detection Of Objects Using Probabilistic Methodsmentioning

confidence: 99%

Secure Data Deduplication System with Cyber Security Multikey Management in Cloud Storage

Kumar¹,

Sathiya²,

Mohanta

et al. 2022

Security and Communication Networks

View full text Add to dashboard Cite

Object detection is essential in a video surveillance system. To recognize an item in a movie, we first examine each picture pixel by pixel. The Secure Data Deduplication system in segmentation is the process of separating distinct picture components into pixels in digital image processing. The performance of segmentation is influenced by irregular and/or poor lighting. These characteristics have a significant impact on the video surveillance system’s real-time object detection process. A multikey management system based on a modified ResNet model is presented in this research (M-ResNet). Cyber security is a suggested algorithm application that is used to improve images that are influenced by a lack of light. The experimental findings reveal a significant improvement in detecting objects in the video stream as compared to the present technique output and modification architecture of the ResNet model. The suggested model achieves superior results in measures like precision, recall, and pixel accuracy, as well as a decent increase in object recognition.

show abstract

Section: Detection Of Objects Using Probabilistic Methodsmentioning

confidence: 99%

Secure Data Deduplication System with Cyber Security Multikey Management in Cloud Storage

Kumar¹,

Sathiya²,

Mohanta

et al. 2022

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…These methods also need some unique denoising methods [30] for any level of noise. Another method [31], extends the probabilistic atlas which provides the healthy tissue information, by latent atlas which provides the lesion information. This generative probabilistic model and discriminative extensions provide semantic meaning to the tissues.…”

Section: Object Detection Using Probabilistic Methodsmentioning

confidence: 99%

Realtime Object Detection Through M-ResNet in Video Surveillance System

Prabu¹,

Gnanasekar²

2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

Object detection plays a vital role in the video surveillance systems. To enhance security, surveillance cameras are now installed in public areas such as traffic signals, roadways, retail malls, train stations, and banks. However, monitoring the video continually at a quicker pace is a challenging job. As a consequence, security cameras are useless and need human monitoring. The primary difficulty with video surveillance is identifying abnormalities such as thefts, accidents, crimes, or other unlawful actions. The anomalous action does not occur at a higher rate than usual occurrences. To detect the object in a video, first we analyze the images pixel by pixel. In digital image processing, segmentation is the process of segregating the individual image parts into pixels. The performance of segmentation is affected by irregular illumination and/or low illumination. These factors highly affect the real-time object detection process in the video surveillance system. In this paper, a modified ResNet model (M-Resnet) is proposed to enhance the image which is affected by insufficient light. Experimental results provide the comparison of existing method output and modification architecture of the ResNet model shows the considerable amount improvement in detection objects in the video stream. The proposed model shows better results in the metrics like precision, recall, pixel accuracy, etc., and finds a reasonable improvement in the object detection.

show abstract

“…The background subtraction method compares two cameracaptured images to detect and track vehicles in the parking area [12]. The background subtraction method is susceptible to changes in light [13], [14]. So in the process, the method uses the concept of reduction by sampling background images under certain conditions, such as morning, afternoon, evening, and night.…”

Section: Introductionmentioning

confidence: 99%

Parking detection system using background subtraction and HSV color segmentation

et al. 2021

View full text Add to dashboard Cite

Manual system vehicle parking makes finding vacant parking lots difficult, so it has to check directly to the vacant space. If many people do parking, then the time needed for it is very much or requires many people to handle it. This research develops a real-time parking system to detect parking. The system is designed using the HSV color segmentation method in determining the background image. In addition, the detection process uses the background subtraction method. Applying these two methods requires image preprocessing using several methods such as grayscaling, blurring (low-pass filter). In addition, it is followed by a thresholding and filtering process to get the best image in the detection process. In the process, there is a determination of the ROI to determine the focus area of the object identified as empty parking. The parking detection process produces the best average accuracy of 95.76%. The minimum threshold value of 255 pixels is 0.4. This value is the best value from 33 test data in several criteria, such as the time of capture, composition and color of the vehicle, the shape of the shadow of the object’s environment, and the intensity of light. This parking detection system can be implemented in real-time to determine the position of an empty place.

show abstract

Background Subtraction Using an Adaptive Local Median Texture Feature in Illumination Changes Urban Traffic Scenes

Cited by 19 publications

References 41 publications

Secure Data Deduplication System with Cyber Security Multikey Management in Cloud Storage

Secure Data Deduplication System with Cyber Security Multikey Management in Cloud Storage

Realtime Object Detection Through M-ResNet in Video Surveillance System

Parking detection system using background subtraction and HSV color segmentation

Contact Info

Product

Resources

About