Moving object tracking using optical flow and motion vector estimation

Kale, Kiran; Pawar, Sanjay B.; Dhulekar, Pravin A.

doi:10.1109/icrito.2015.7359323

Cited by 80 publications

(30 citation statements)

References 4 publications

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“…In addition, as [23] suggests, the use of image gradients might improve the performance by achieving better parallax suppression. Besides, optical flow [24] might be incorporated into the moving object detection block at the price of computational power.…”

Section: Discussionmentioning

confidence: 99%

Real-Time Detection, Tracking and Classification of Multiple Moving Objects in UAV Videos

Baykara¹,

Bıyık²,

Gul³

et al. 2017

2017 IEEE 29th International Conference on Tools With Artificial Intelligence (ICTAI)

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Unnamed Aerial Vehicles (UAVs) are becoming increasingly popular and widely used for surveillance and reconnaissance. There are some recent studies regarding moving object detection, tracking, and classification from UAV videos. A unifying study, which also extends the application scope of such previous works and provides real-time results, is absent from the literature. This paper aims to fill this gap by presenting a framework that can robustly detect, track and classify multiple moving objects in real-time, using commercially available UAV systems and a common laptop computer. The framework can additionally deliver practical information about the detected objects, such as their coordinates and velocities. The performance of the proposed framework, which surpasses human capabilities for moving object detection, is reported and discussed.

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Section: Discussionmentioning

confidence: 99%

Real-Time Detection, Tracking and Classification of Multiple Moving Objects in UAV Videos

Baykara¹,

Bıyık²,

Gul³

et al. 2017

2017 IEEE 29th International Conference on Tools With Artificial Intelligence (ICTAI)

View full text Add to dashboard Cite

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“…Motion Detection Using Horn Schunck Algorithm and Implementation [8] by examining an optical flow image that the optimal parameters resembling smoothness, iteration compactness for other types of displacements like trivial, medium, huge has to be evaluated by utilizing an Horn Schunck algorithm. Moving Object Tracking using Optical Flow and Motion Vector Estimation [9] that moving object detection and tracking is an developing exploration field since it has vast applications in traffic inspection, 3D observation, movement investigation (human and non-human), activity recognition, therapeutic imaging etc. designing a modern object perception and tracking algorithm which utilized optical flow belongs to motion vector estimation for object detection and tracking in a successive frames.…”

Section: Karthika Pragadeeswari Gyamuna G Yasmin Behammentioning

confidence: 99%

A Robust Algorithm for Real Time Tracking With Optical Flow

Pragadeeswari¹,

Yamuna²,

Beham³

2019

IJITEE

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Object tracking is a troublesome undertaking and significant extent in data processor perception and image handling community. Some of the applications are protection surveillance, traffic monitoring on roads, offense detection and medical imaging. In this paper a recent technique for tracking of moving object is intended. Optical flow information authorizes us to know the displacement and speed of objects personate in a scene. Apply optical flow to the image gives flow vectors of the points to distinguishing the moving aspects. Optical flow is accomplished by Lucas canade algorithm. This algorithm is superior to other algorithms. The outcomes reveals that the intend algorithm is efficient and accurate object tracking method. This paper depicts a smoothing algorithm to track the moving object of both single and multiple objects in real time. The main issue of high computational time is greatly reduced in this proposed work

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“…Many methods have been developed that use classic computer vision and machine learning approaches for object tracking. Kale et al (2015) [11] utilized a classic optical-flow algorithm as well as motion vector estimation to solve the object tracking problems. They proposed a track-by-detect approach, where detection was done by using an optical-flow algorithm and speed estimation was handled by motion vector estimation.…”

Section: Related Workmentioning

confidence: 99%

Vehicle Tracking and Speed Estimation from Traffic Videos

Hua

Kapoor

Anastasiu

2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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The rapid recent advancements in the computation ability of everyday computers have made it possible to widely apply deep learning methods to the analysis of traffic surveillance videos. Traffic flow prediction, anomaly detection, vehicle re-identification, and vehicle tracking are basic components in traffic analysis. Among these applications, traffic flow prediction, or vehicle speed estimation, is one of the most important research topics of recent years. Good solutions to this problem could prevent traffic collisions and help improve road planning by better estimating transit demand. In the 2018 NVIDIA AI City Challenge, we combine modern deep learning models with classic computer vision approaches to propose an efficient way to predict vehicle speed. In this paper, we introduce some state-of-the-art approaches in vehicle speed estimation, vehicle detection, and object tracking, as well as our solution for Track 1 of the Challenge.

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Moving object tracking using optical flow and motion vector estimation

Cited by 80 publications

References 4 publications

Real-Time Detection, Tracking and Classification of Multiple Moving Objects in UAV Videos

Real-Time Detection, Tracking and Classification of Multiple Moving Objects in UAV Videos

A Robust Algorithm for Real Time Tracking With Optical Flow

Vehicle Tracking and Speed Estimation from Traffic Videos

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