Detection of the movements of persons from a sparse sequence of TV images

Tsukiyama, Toshifumi; Shirai, Yoshiaki

doi:10.1016/0031-3203(85)90046-9

Cited by 33 publications

(7 citation statements)

References 2 publications

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“…Detecting people in images is a problem with a long history [37,13,35,27,16,41,23,5]; in the past two years there has been a surge of interest in pedestrian detection [6,9,11,18,20,21,25,30,32,33,36,38,42]. Accurate pedestrian detection would have immediate and far reaching impact to applications such as surveillance, robotics, assistive technology for the visually impaired, content based indexing (e.g.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian detection: A benchmark

Dollár

Wojek

Schiele

et al. 2009

2009 IEEE Conference on Computer Vision and Pattern Recognition

1,137

556

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Pedestrian detection is a key problem in computer vision, with several applications including robotics, surveillance and automotive safety. Much of the progress of the past few years has been driven by the availability of challenging public datasets. To continue the rapid rate of innovation, we introduce the Caltech Pedestrian Dataset, which is two orders of magnitude larger than existing datasets. The dataset contains richly annotated video, recorded from a moving vehicle, with challenging images of low resolution and frequently occluded people. We propose improved evaluation metrics, demonstrating that commonly used perwindow measures are flawed and can fail to predict performance on full images. We also benchmark several promising detection systems, providing an overview of state-of-theart performance and a direct, unbiased comparison of existing methods. Finally, by analyzing common failure cases, we help identify future research directions for the field. 1 www.vision.caltech.edu/Image Datasets/CaltechPedestrians/ 1 304 978-1-4244-3991-1/09/$25.00 ©2009 IEEE

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Section: Introductionmentioning

confidence: 99%

Pedestrian detection: A benchmark

Dollár

Wojek

Schiele

et al. 2009

2009 IEEE Conference on Computer Vision and Pattern Recognition

1,137

556

View full text Add to dashboard Cite

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“…The wavelet representation was calculated from statistical analysis of class instances. Another approach by Shirai et al [ 31 ] for detecting objects required manual parameter tuning to find all objects and needed a few assumptions, such as the type and shape of an object, prior to detection.…”

Section: Related Workmentioning

confidence: 99%

Exploiting Concepts of Instance Segmentation to Boost Detection in Challenging Environments

Hashmi

Pagani

Liwicki

et al. 2022

Sensors

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In recent years, due to the advancements in machine learning, object detection has become a mainstream task in the computer vision domain. The first phase of object detection is to find the regions where objects can exist. With the improvements in deep learning, traditional approaches, such as sliding windows and manual feature selection techniques, have been replaced with deep learning techniques. However, object detection algorithms face a problem when performed in low light, challenging weather, and crowded scenes, similar to any other task. Such an environment is termed a challenging environment. This paper exploits pixel-level information to improve detection under challenging situations. To this end, we exploit the recently proposed hybrid task cascade network. This network works collaboratively with detection and segmentation heads at different cascade levels. We evaluate the proposed methods on three complex datasets of ExDark, CURE-TSD, and RESIDE, and achieve a mAP of 0.71, 0.52, and 0.43, respectively. Our experimental results assert the efficacy of the proposed approach.

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“…Tracking is a process of enabling a continuous relationship of the target and their connecting links between video frames. It is a separate process, as a tool to prepare data for object recognition [7]. It involves detecting the moving objects by identifying the moving objects and tracking them with respect to time.…”

Section: Trackingmentioning

confidence: 99%

Advances in Motion Detection and Tracking of Human as a Target

Biju¹,

Shanthi²

2019

IJITEE

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Motion estimation of a target is the major area with higher computational complexity in video processing. It is the progression of discovery the motion patterns that describe the transformation from one frame to another in a sequence of video. Therefore, it is reasonable to carry out motion estimation only where movement is present. Image data in an image series remains mostly the same between frames along the target motion. To make use of the image statistics redundancy in image sequences, there is a need to guess motion. Motion estimation is valid for video compression improvement, stereo correspondence, object tracking and finding optical flow. Many precise methods have been proposed in the framework of one or more of these applications. Most motion estimation algorithms either operate directly in the image domain or finding the similar metric that measures how alike two pixels or two patches of pixels. In this paper, a review of a variety of motion estimation technique is presented.

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Detection of the movements of persons from a sparse sequence of TV images

Cited by 33 publications

References 2 publications

Pedestrian detection: A benchmark

Pedestrian detection: A benchmark

Exploiting Concepts of Instance Segmentation to Boost Detection in Challenging Environments

Advances in Motion Detection and Tracking of Human as a Target

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