181,259 research outputs found
Vision-based Real-Time Aerial Object Localization and Tracking for UAV Sensing System
The paper focuses on the problem of vision-based obstacle detection and
tracking for unmanned aerial vehicle navigation. A real-time object
localization and tracking strategy from monocular image sequences is developed
by effectively integrating the object detection and tracking into a dynamic
Kalman model. At the detection stage, the object of interest is automatically
detected and localized from a saliency map computed via the image background
connectivity cue at each frame; at the tracking stage, a Kalman filter is
employed to provide a coarse prediction of the object state, which is further
refined via a local detector incorporating the saliency map and the temporal
information between two consecutive frames. Compared to existing methods, the
proposed approach does not require any manual initialization for tracking, runs
much faster than the state-of-the-art trackers of its kind, and achieves
competitive tracking performance on a large number of image sequences.
Extensive experiments demonstrate the effectiveness and superior performance of
the proposed approach.Comment: 8 pages, 7 figure
Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification
Online multi-object tracking is a fundamental problem in time-critical video
analysis applications. A major challenge in the popular tracking-by-detection
framework is how to associate unreliable detection results with existing
tracks. In this paper, we propose to handle unreliable detection by collecting
candidates from outputs of both detection and tracking. The intuition behind
generating redundant candidates is that detection and tracks can complement
each other in different scenarios. Detection results of high confidence prevent
tracking drifts in the long term, and predictions of tracks can handle noisy
detection caused by occlusion. In order to apply optimal selection from a
considerable amount of candidates in real-time, we present a novel scoring
function based on a fully convolutional neural network, that shares most
computations on the entire image. Moreover, we adopt a deeply learned
appearance representation, which is trained on large-scale person
re-identification datasets, to improve the identification ability of our
tracker. Extensive experiments show that our tracker achieves real-time and
state-of-the-art performance on a widely used people tracking benchmark.Comment: ICME 201
On the Real Time Object Detection and Tracking
Object detection and tracking is widely used for detecting motions of objects present in images and video.Since last so many decades, numerous real time object detection and tracking methods have been proposed byresearchers. The proposed methods for objects to be tracked till date require some preceding informationassociated with moving objects. In real time object detection and tracking approach segmentation is the initialtask followed by background modeling for the extraction of predefined information including shape of the objects,position in the starting frame, texture, geometry and so on for further processing of the cluster pixels and videosequence of these objects. The object detection and tracking can be applied in the fields like computerized videosurveillance, traffic monitoring, robotic vision, gesture identification, human-computer interaction, militarysurveillance system, vehicle navigation, medical imaging, biomedical image analysis and many more. In thispaper we focus detailed technical review of different methods proposed for detection and tracking of objects. Thecomparison of various techniques of detection and tracking is the purpose of this work
Vehicle Tracking Using Video Surveillance
In numerous applications including the security of individual vehicles as well as public transportation frameworks, the ability to follow or track vehicles is very helpful. Using computer vision and deep learning algorithms, the project deals with the concept of vehicle tracking in real-time based on continuous video stream from a CCTV camera to track the vehicles. The tracking system is tracking by detection paradigm. YOLOv3 object detection is applied to achieve faster object detection for real-time tracking. By implementing and improving the ideas of Deep SORT tracking for better occlusion handling, a better tracking system suitable for real-time vehicle tracking is presented. So as to demonstrate the achievability and adequacy of the framework, this chapter presents exploratory consequences of the vehicle following framework and a few encounters on handy executions
Real Time Adaptive Tracking System Using Computer Vision
This project studies long-time object tracking in a sequence of frames. In this project, a detector is trained with specimens found on the path of a tracker that itself does not rely on the object detector. We attain high robustness and outdo current adaptive tracking-by-detection (11) approaches by decoupling object tracking and object detection. A substantial reduction of calculating time is attained by means of simple features for object detection and by using a cascaded method. The object location is marked in each frame. The task is to find the position of object in that frame else it must notify that the object is absent in the consecutive frames. We have developed a Real Time Tracking framework. The task of long-time tracking is divided as follows: Tracking, Learning and Detection. The tracker must follow the marked object of interest in consecutive frames. The detector restricts all observed appearances and amends the tracker when required. To evade these blunders there forth, the learning approximates the detector’s blunders and re-evaluates it. This project studies methods to recognize the detector’s faults and learn from, by developing a learning method with the help of “experts” which will estimate these blunders. We call it the P-N learning. With the help of RAT and P-N learning, our real-time processing can be described as an extremely integrated arrangement providing very precise object detection with RGB-D sensor
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