Object Detection and Tracking-Based Camera Calibration for Normalized Human Height Estimation

Jung, Jae Hwan; Yoon, Inhye; Lee, Sangkeun; Paik, Joonki

doi:10.1155/2016/8347841

Cited by 13 publications

(7 citation statements)

References 28 publications

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“…While most of these can estimate occupancy patterns reliably, they cannot be employed in most office buildings for reasons like privacy and cost concerns. Jaehoon Jung et al [12] show a normalized human height estimation algorithm using uncalibrated cameras. The algorithm tracks moving objects and carries out tracking-based automatic camera calibration.…”

Section: State Of the Artmentioning

confidence: 99%

Estimating Occupancy from Measurements and Knowledge Using the Bayesian Network for Energy Management

et al. 2019

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A general approach is proposed to determine occupant behavior (occupancy and activity) in offices and residential buildings in order to use these estimates for improved energy management. Occupant behavior is modelled with a Bayesian network in an unsupervised manner. This algorithm makes use of domain knowledge gathered via questionnaires and recorded sensor data for motion detection, power, and hot water consumption as well as indoor CO2 concentration. Different case studies have been investigated with diversity according to their context (available sensors, occupancy or activity feedback, complexity of the environment, etc.). Furthermore, experiments integrating occupancy estimation and hot water production control show that energy efficiency can be increased by roughly 5% over known optimal control techniques and more than 25% over rule-based control while maintaining the same occupant comfort.

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Section: State Of the Artmentioning

confidence: 99%

Estimating Occupancy from Measurements and Knowledge Using the Bayesian Network for Energy Management

et al. 2019

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“…A normalized human height estimation algorithm using an uncalibrated camera was introduced in [11]. The normalized human height is estimated using multiple uncalibrated cameras.…”

Section: Related Workmentioning

confidence: 99%

“…In equation (11), " ", is the normalization factor and can be defined as follows:-, Here, the values of the variables ranging from 0 ~ 8 represents the various image features of objects. From the extracted image features, the variable values are computed.…”

Section: Kernel Pattern Segment Function For Moving Object Detectionmentioning

confidence: 99%

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Quantized Kalman Filter-Based Pattern Matching for Detection and Tracking of Moving Objects

Rajamanickam*

2019

IJEAT

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Detection And Tracking Of Multiple Moving Objects From A Sequence Of Video Frame And Obtaining Visual Records Of Objects Play An Important Role In The Video Surveillance Systems. Transform And Filtering Technique Designed For Video Pattern Matching And Moving Object Detection, Failed To Handle Large Number Of Objects In Video Frame And Further Needs To Be Optimized. Several Existing Methods Perform Detection And Tracking Of Moving Objects. However, The Performance Efficiency Of The Existing Methods Needs To Be Optimized To Achieve More Robust And Reliable Detection And Tracking Of Moving Objects. In Order To Improve The Pattern Matching Accuracy, A Quantized Kalman Filter-Based Pattern Matching (Qkf-Pm) Technique Is Proposed For Detecting And Tracking Of Moving Objects. The Present Phase Includes Three Functionalities: Top-Down Approach, Kernel Pattern Segment Function And Kalman Filtering. First, The Top-Down Approach Based On Kalman Filtering (Kf) Technique Is Performed To Detect The Chromatic Shadows Of Objects. Next, Kernel Pattern Segment Function Creates The Seed Points For Detecting Moving Object Pattern. Finally, Object Tracking Is Performed Using The Proposed Quantized Kalman Filter Based On The Center Of Seed Point Affinity Feature Values Are Used To Track The Moving Objects In A Particular Region Using The Minimum Bounding Box Approach. Experimental Results Reveals That The Proposed Qkf-Pm Technique Achieves Better Performance In Terms Of True Detection Rate, Pattern Matching Accuracy, Pattern Matching Time, And Object Tracking Accuracy With Respect To The Number Of Video Frames Per Second.

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“…Using the cameras, moving objects such as vehicles and pedestrians can be recognized and location information can be derived from a map. It can be used in a variety of approaches for traffic surveillance camera services such as the detection of unexpected blind spots and pedestrian care [10,11]. In this regard, research on object recognition and image analysis based on deep learning for road traffic information analysis is steadily progressing.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Driving Assistance with Dynamic Objects Using Traffic Surveillance Cameras

Cho¹,

Cho

2022

Applied Sciences

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This paper describes a method that precisely estimates the position of images of traffic surveillance camera objects. We suggest a projection method with multiple traffic surveillance cameras through a local coordinate system into a global coordinate system. The transformation of coordinates uses detected objects, parameters of the camera and the geometric information of high- definition (HD) maps. Traffic surveillance cameras that pursue traffic safety and convenience use various sensors to generate traffic information. We suggest a transformation method with images of the camera and HD maps and an evaluation method. Therefore, it is necessary to improve the sensor-related technology to increase the efficiency and reliability of the traffic information. Recently, the role of the camera in collecting video information has become more important due to advances in artificial intelligence (AI) technology. The objects projected from the traffic surveillance camera domain to the HD domain are helpful to identify imperceptible zones, such as blind spots, on roads for autonomous driving assistance. In this study, we proposed to identify and track dynamic objects (vehicles, pedestrian, etc.) with traffic surveillance cameras, and to analyze and provide information about them in various environments. To this end, we conducted the identification of dynamic objects using the Yolov4 and DeepSort algorithms, established real-time multi-user support servers based on Kafka, defined transformation matrices between images and spatial coordinate systems, and implemented map-based dynamic object visualization. In addition, a positional consistency evaluation was performed to confirm its usefulness. Through the proposed scheme, we confirmed that multiple traffic surveillance cameras can serve as important sensors to provide relevant information by analyzing road conditions in real-time in terms of road infrastructure beyond a simple monitoring role.

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Object Detection and Tracking-Based Camera Calibration for Normalized Human Height Estimation

Cited by 13 publications

References 28 publications

Estimating Occupancy from Measurements and Knowledge Using the Bayesian Network for Energy Management

Estimating Occupancy from Measurements and Knowledge Using the Bayesian Network for Energy Management

Quantized Kalman Filter-Based Pattern Matching for Detection and Tracking of Moving Objects

Autonomous Driving Assistance with Dynamic Objects Using Traffic Surveillance Cameras

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