Optical Flow Based Object Movement Tracking

Balasundaram, A.; Kumar, S. Ashok; Kumar, S. Magesh

doi:10.35940/ijeat.a1317.109119

Cited by 19 publications

(8 citation statements)

References 11 publications

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“…Edge detection Project 3D model onto a 2D image, matching with the features of the corresponding edge, and calculate 3D camera motion between frames based on 2D displacement of the corresponding feature, to realize pose tracking [12] Point of interest tracking Identify the features of target points from the image database, and then save the location and virtual information; extract feature points in the current view, and match them with the features in the database, to estimate the camera pose [13] Template matching rough recognizing the texture information in the camera view, match with the most relevant images in the image database, to estimate the camera pose [14] Optical flow tracking Under the premise of the constant spatial projection intensity, the physical point in video sequence can be tracked through measuring the speed of pixel position change in the path of projecting a 3D object onto a 2D plane, so as to complete pose tracking [15] Depth imaging Generate depth images with the reference pixel value of the distance between the camera view and the object, and integrate the depth images with RGB images for estimating the camera pose [16] Model-free tracking is method can realize tracking without a model or database; the reconstruct 3D structure of the images through tracking the focal length, the rotation matrix, and the translation vector of the camera, and perform triangulation between the corresponding points of each image, to calculate the camera pose [17] (a) (b) 5) and set multiple guides based on beacon positioning, to meet the needs of users for exhibit explanation and floor navigation. Sun et al [24] designed a method of intelligent spacing selection model, which can improve the problems of high delay and high energy consumption in the Internet of ings.…”

Section: Natural Feature-based Tracking Registration Methods Realizat...mentioning

confidence: 99%

A Survey of Museum Applied Research Based on Mobile Augmented Reality

Wang

Zhu

2022

Computational Intelligence and Neuroscience

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Museums are the important places of education for preservation and dissemination of human material and intangible heritage. Nowadays, there are many problems in museums such as single display mode and fixed interaction style of collections, which makes cognitive load higher and visiting experience poorer for tourists. Mobile Augmented Reality is a technology that seamlessly integrates virtual information with real environment based on mobile devices. Museum applications based on Mobile Augmented Reality can improve the display richness and meet new visiting needs of the public in the digital era. Firstly, this article introduces the key technologies of Mobile Augmented Reality and its use in museums. Secondly, according to different application functions, classifiable explanations for Mobile Augmented Reality application examples are given. Thirdly, based on the progress in the application of Mobile Augmented Reality in museums at home and abroad, from three dimensions of content, guide, and interaction, designing thought regarding the application of Mobile Augmented Reality for museums is summarized.

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Section: Natural Feature-based Tracking Registration Methods Realizat...mentioning

confidence: 99%

A Survey of Museum Applied Research Based on Mobile Augmented Reality

Wang

Zhu

2022

Computational Intelligence and Neuroscience

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“…Despite the existence of various technological wonders to handle this issue, the appropriate system is still needed to resolve the problem. Balasundaram et al [7] have presented a system that makes use of a multi-task cascade neural network to identify human faces and a Siamese neural network approach to retrieve the identity of a person. Initially, this real-time methodology converts the live video into image frames on which face detection is performed.…”

Section: Literature Surveymentioning

confidence: 99%

Deep Learning Based Face Detection and Identification of Criminal Suspects

Sandhya¹,

Balasundaram²,

Shaik³

2023

Computers, Materials &Amp; Continua

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Occurrence of crimes has been on the constant rise despite the emerging discoveries and advancements in the technological field in the past decade. One of the most tedious tasks is to track a suspect once a crime is committed. As most of the crimes are committed by individuals who have a history of felonies, it is essential for a monitoring system that does not just detect the person's face who has committed the crime, but also their identity. Hence, a smart criminal detection and identification system that makes use of the OpenCV Deep Neural Network (DNN) model which employs a Single Shot Multibox Detector for detection of face and an auto-encoder model in which the encoder part is used for matching the captured facial images with the criminals has been proposed. After detection and extraction of the face in the image by face cropping, the captured face is then compared with the images in the Criminal Database. The comparison is performed by calculating the similarity value between each pair of images that are obtained by using the Cosine Similarity metric. After plotting the values in a graph to find the threshold value, we conclude that the confidence rate of the encoder model is 0.75 and above.

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“…Lastly, they use the location values to calculate the object's location and speed. Optical flow techniques are classified into two types: sparse optical flow techniques and dense optical flow techniques [42]. Sparse Optical flow technique provides flow vectors of some "interesting features" within the image and only need to process a subset of the pixels in the image, which is used on paper, whereas dense techniques process all pixels.…”

Section: Tracking Using Optical Flowmentioning

confidence: 99%

Enhanced aerial vehicle system techniques for detection and tracking in fog, sandstorm, and snow conditions

Talaat

El–Sappagh

2023

J Supercomput

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Unmanned aerial vehicles are rapidly being utilized in surveillance and traffic monitoring because of their great mobility and capacity to cover regions at various elevations and positions. It is a challenging task to detect vehicles due to their various shapes, textures, and colors. One of the most difficult challenges is correctly detecting and counting aerial view vehicles in real time for traffic monitoring objectives using aerial images and videos. In this research, strategies are presented for improving the detection ability of self-driving vehicles in tough conditions, also for traffic monitoring, vehicle surveillance. We make classification, tracking trajectories, and movement calculation where fog, sandstorm (dust), and snow conditions are challenging. Initially, image enhancement methods are implemented to improve unclear images of roads. The improved images are then subjected to an object detection and classification algorithm to detect vehicles. Finally, new methods were evaluated (Corrected Optical flow/Corrected Kalman filter) to get the least error of trajectories. Also features like vehicle count, type, tracking trajectories by (Optical flow, Kalman Filter, Euclidean Distance) and relative movement calculation are extracted from the coordinates of the observed objects. These techniques aim to improve vehicle detection, tracking, and movement over aerial views of roads especially in bad weather. As a result, for aerial view vehicles in bad weather, our proposed method has an error of less than 5 pixels from the actual value and give the best results. This improves detection and tracking performance for aerial view vehicles in bad weather conditions.

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Optical Flow Based Object Movement Tracking

Cited by 19 publications

References 11 publications

A Survey of Museum Applied Research Based on Mobile Augmented Reality

A Survey of Museum Applied Research Based on Mobile Augmented Reality

Deep Learning Based Face Detection and Identification of Criminal Suspects

Enhanced aerial vehicle system techniques for detection and tracking in fog, sandstorm, and snow conditions

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