Enhancing Detection Quality Rate with a Combined HOG and CNN for Real-Time Multiple Object Tracking across Non-Overlapping Multiple Cameras

Kalake, Lesole; Dong, Yanqiu; Wan, Wanggen; Hou, Long

doi:10.3390/s22062123

Cited by 11 publications

(5 citation statements)

References 33 publications

(52 reference statements)

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“…The second increases the number of salient pedestrian features to improve classification ability [35]. Local binary patterns (LBP) [36], stereo disparity features [34], and convolutional neural networks (CNN) [37] are examples of feature extractors.…”

Section: Results and Analysismentioning

confidence: 99%

FPGA-Based Pedestrian Detection for Collision Prediction System

Cambuim

Barros

2022

Sensors

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Pedestrian detection (PD) systems capable of locating pedestrians over large distances and locating them faster are needed in Pedestrian Collision Prediction (PCP) systems to increase the decision-making distance. This paper proposes a performance-optimized FPGA implementation of a HOG-SVM-based PD system with support for image pyramids and detection windows of different sizes to locate near and far pedestrians. This work proposes a hardware architecture that can process one pixel per clock cycle by exploring data and temporal parallelism using techniques such as pipeline and spatial division of data between parallel processing units. The proposed architecture for the PD module was validated in FPGA and integrated with the stereo semi-global matching (SGM) module, also prototyped in FPGA. Processing two windows of different dimensions permitted a reduction in miss rate of at least 6% compared to a uniquely sized window detector. The performances achieved by the PD system and the PCP system in HD resolution were 100 and 66.2 frames per second (FPS), respectively. The performance improvement achieved by the PCP system with the addition of our PD module permitted an increase in decision-making distance of 3.3 m compared to a PCP system that processes at 30 FPS.

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Section: Results and Analysismentioning

confidence: 99%

FPGA-Based Pedestrian Detection for Collision Prediction System

Cambuim

Barros

2022

Sensors

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“…Then normalization is carried out on each block feature that has been obtained. Fourth, the results of the normalization of all block features are combined into one HOG feature [26].…”

Section: Feature Extractionmentioning

confidence: 99%

A Combination of Transfer Learning and Support Vector Machine for Robust Classification on Small Weed and Potato Datasets

Adhinata¹,

Ramadhan²,

Fauzi³

et al. 2023

JOIV : Int. J. Inform. Visualization

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Agriculture is the primary sector in Indonesia for meeting people's daily food demands. One of the agricultural commodities that replace rice is potatoes. Potato growth needs to be protected from weeds that compete for nutrients. Spraying using pesticides can cause environmental pollution, affecting cultivated plants. Currently, agricultural technology is being developed using an Artificial Intelligence (AI) approach to classifying crops. The classification process using AI depends on the number of datasets obtained. The number of datasets obtained in this research is not too large, so it requires a particular approach regarding the AI method used. This research aims to use a combination of feature extraction methods with local and deep feature approaches with supervised machine learning to classify of small datasets. The local feature method used in this research is Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG), while the deep feature method used is MobileNet and MobileNetV2. The famous Support Vector Machine (SVM) uses the classification method to separate two data classes. The experimental results showed that the local feature HOG method was the fastest in the training process. However, the most accurate result was using the MobileNetV2 deep feature method with an accuracy of 98%. Deep features produced the best accuracy because the feature extraction process went through many neural network layers. This research can provide insight on how to analyze a small number of datasets by combining several strategies

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“…TrafficSensor accurately detects and classifies the objects within images using various versions of YOLO. Kalake et al (2022) propose a paradigm aimed at eliminating object tracking difficulties by enhancing the detection quality rate through the combination of a convolutional neural network (CNN) and a histogram of oriented gradient (HOG) descriptor. Jiang et al (2022) propose a novel method to continuously track several mice and individual parts without requiring any specific tagging.…”

Section: Object Tracking In Videomentioning

confidence: 99%

Automation of Computer Vision Applications for Real-time Combat Sports Video Analysis

Quinn

Corcoran

2022

ECIAIR

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This study examines the potential applications of Human Action Recognition (HAR) in combat sports and aims to develop a prototype automation client that examines a video of a combat sports competition or training session and accurately classifies human movements. Computer Vision (CV) architectures that examine real-time video data streams are being investigated by integrating Deep Learning architectures into client-server systems for data storage and analysis using customised algorithms. The development of the automation client for training and deploying CV robots to watch and track specific chains of human actions is a central component of the project. Categorising specific chains of human actions allows for the comparison of multiple athletes' techniques as well as the identification of potential areas for improvement based on posture, accuracy, and other technical details, which can be used as an aid to improve athlete efficiency. The automation client will also be developed for the purpose of scoring, with a focus on the automation of the CV model to analyse and score a competition using a specific ruleset. The model will be validated by comparing performance and accuracy to that of combat sports experts. The primary research domains are CV, automation, robotics, combat sports, and decision science. Decision science is a set of quantitative techniques used to assist people to make decisions. The creation of a new automation client may contribute to the development of more efficient machine learning and CV applications in areas such as process efficiency, which improves user experience, workload management to reduce wait times, and run-time optimisation. This study found that real-time object detection and tracking can be combined with real-time pose estimation to generate performance statistics from a combat sports athlete's movements in a video.

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Enhancing Detection Quality Rate with a Combined HOG and CNN for Real-Time Multiple Object Tracking across Non-Overlapping Multiple Cameras

Cited by 11 publications

References 33 publications

FPGA-Based Pedestrian Detection for Collision Prediction System

FPGA-Based Pedestrian Detection for Collision Prediction System

A Combination of Transfer Learning and Support Vector Machine for Robust Classification on Small Weed and Potato Datasets

Automation of Computer Vision Applications for Real-time Combat Sports Video Analysis

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