Multiple human tracking in wearable camera videos with informationless intervals

Yu, Hongkai; Yu, Haozhou; Guo, Hao; Simmons, Jeff; Zou, Qin; Feng, Wei; Wang, Song

doi:10.1016/j.patrec.2018.06.003

Cited by 5 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many current computer vision benchmarks have been proposed for human related factors, such as human detection, [33] human pose estimation, [34] human action recognition, [19,35] human tracking, [36,37] and so forth. However, their problem definitions are different with phone-related pedestrian distracted behaviour recognition.…”

Section: Benchmark Datasetmentioning

confidence: 99%

Detecting phone‐related pedestrian distracted behaviours via a two‐branch convolutional neural network

Saenz

Sun

Wu³

et al. 2020

IET Intelligent Trans Sys

Self Cite

View full text Add to dashboard Cite

The distracted phone‐use behaviours among pedestrians, like Texting, Game Playing and Phone Calls, have caused increasing fatalities and injuries. However, the research of phone‐related distracted behaviour by pedestrians has not been systemically studied. It is desired to improve both the driving and pedestrian safety by automatically discovering the phone‐related pedestrian distracted behaviours. Herein, a new computer vision‐based method is proposed to detect the phone‐related pedestrian distracted behaviours from a view of intelligent and autonomous driving. Specifically, the first end‐to‐end deep learning based Two‐Branch Convolutional Neural Network (CNN) is designed for this task. Taking one synchronised image pair by two front on‐car GoPro cameras as the inputs, the proposed two‐branch CNN will extract features for each camera, fuse the extracted features and perform a robust classification. This method can also be easily extended to video‐based classification by confidence accumulation and voting. A new benchmark dataset of 448 synchronised video pairs of 53,760 images collected on a vehicle is proposed for this research. The experimental results show that using two synchronised cameras obtained better performance than using one single camera. Finally, the proposed method achieved an overall best classification accuracy of 84.3% on the new benchmark when compared to other methods.

show abstract

Section: Benchmark Datasetmentioning

confidence: 99%

Detecting phone‐related pedestrian distracted behaviours via a two‐branch convolutional neural network

Saenz

Sun

Wu³

et al. 2020

IET Intelligent Trans Sys

Self Cite

View full text Add to dashboard Cite

show abstract

“…With the success of deep learning in visual perception and recognition, a number of deep learning-based object detection methods have been proposed in the past decade [1][2][3] and have been applied to many tasks, such as vehicle detection [4,5], pedestrian detection [6][7][8], and so on. Object detection based on supervised deep learning requires a large number of labeled samples for training [9,10].…”

Section: Introductionmentioning

confidence: 99%

“…Although the various detectors have shown their high performance in vehicle detection [17][18][19], pedestrian detection [6][7][8], and line detection [20][21][22], there are still some problems when handling the task of ship detection [23,24]. The main reason is the changing of scale.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Scale Sensitivity of Ship Detection in an Anchor-Free Deep Learning Framework

Jiang¹,

Huang

Zhang³

et al. 2022

Electronics

View full text Add to dashboard Cite

Ship detection is an important task in sea surveillance. In the past decade, deep learning-based methods have been proposed for ship detection from images and videos. Convolutional features are observed to be very effective in representing ship objects. However, the scales of convolution often lead to different capacities of feature representation. It is unclear how the scale influences the performance of deep learning methods in ship detection. To this end, this paper studies the scale sensitivity of ship detection in an anchor-free deep learning framework. Specifically, we employ the classical CenterNet as the base and analyze the influence of the size, the depth, and the fusion strategy of convolution features on multi-scale ship target detection. Experiments show that, for small targets, the features obtained from the top-down path fusion can improve the detection performance more significantly than that from the bottom-up path fusion; on the contrary, the bottom-up path fusion achieves better detection performance on larger targets.

show abstract

SmartSORT: an MLP-based method for tracking multiple objects in real-time

Meneses

Matos

Prado

et al. 2021

J Real-Time Image Proc

View full text Add to dashboard Cite

Multiple human tracking in wearable camera videos with informationless intervals

Cited by 5 publications

References 9 publications

Detecting phone‐related pedestrian distracted behaviours via a two‐branch convolutional neural network

Detecting phone‐related pedestrian distracted behaviours via a two‐branch convolutional neural network

Analysis of Scale Sensitivity of Ship Detection in an Anchor-Free Deep Learning Framework

SmartSORT: an MLP-based method for tracking multiple objects in real-time

Contact Info

Product

Resources

About