Tracking People by Detection Using CNN Features

Chahyati, Dina; Fanany, Mohamad Ivan; Arymurthy, Aniati Murni

doi:10.1016/j.procs.2017.12.143

Cited by 45 publications

(13 citation statements)

References 3 publications

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“…1. It follows the tracking-by-detection approach used by related works for active camera control [7,16]. In this paradigm an object detector is used to localize the targets and a tracker is applied to filter the detections over time.…”

Section: Discussionmentioning

confidence: 99%

$$\text{C}^{3}\text{Net}$$: end-to-end deep learning for efficient real-time visual active camera control

Kyrkou

2021

J Real-Time Image Proc

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The need for automated real-time visual systems in applications such as smart camera surveillance, smart environments, and drones necessitates the improvement of methods for visual active monitoring and control. Traditionally, the active monitoring task has been handled through a pipeline of modules such as detection, filtering, and control. However, such methods are difficult to jointly optimize and tune their various parameters for real-time processing in resource constraint systems. In this paper a deep Convolutional Camera Controller Neural Network is proposed to go directly from visual information to camera movement to provide an efficient solution to the active vision problem. It is trained end-to-end without bounding box annotations to control a camera and follow multiple targets from raw pixel values. Evaluation through both a simulation framework and real experimental setup, indicate that the proposed solution is robust to varying conditions and able to achieve better monitoring performance than traditional approaches both in terms of number of targets monitored as well as in effective monitoring time. The advantage of the proposed approach is that it is computationally less demanding and can run at over 10 FPS ( ∼ 4× speedup) on an embedded smart camera providing a practical and affordable solution to real-time active monitoring.

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Section: Discussionmentioning

confidence: 99%

$$\text{C}^{3}\text{Net}$$: end-to-end deep learning for efficient real-time visual active camera control

Kyrkou

2021

J Real-Time Image Proc

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“…In this paper, we are interested in studies using CNNs to track people in the video. Dina et al [66] experimented with a method for tracking multiple persons in images using Faster R-CNN to detect people. The results of people detection in the image are shown as bounding boxes.…”

Section: Methodsmentioning

confidence: 99%

Human Segmentation and Tracking Survey on Masks for MADS Dataset

Scherer

2021

Sensors

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Human segmentation and tracking often use the outcome of person detection in the video. Thus, the results of segmentation and tracking depend heavily on human detection results in the video. With the advent of Convolutional Neural Networks (CNNs), there are excellent results in this field. Segmentation and tracking of the person in the video have significant applications in monitoring and estimating human pose in 2D images and 3D space. In this paper, we performed a survey of many studies, methods, datasets, and results for human segmentation and tracking in video. We also touch upon detecting persons as it affects the results of human segmentation and human tracking. The survey is performed in great detail up to source code paths. The MADS (Martial Arts, Dancing and Sports) dataset comprises fast and complex activities. It has been published for the task of estimating human posture. However, before determining the human pose, the person needs to be detected as a segment in the video. Moreover, in the paper, we publish a mask dataset to evaluate the segmentation and tracking of people in the video. In our MASK MADS dataset, we have prepared 28 k mask images. We also evaluated the MADS dataset for segmenting and tracking people in the video with many recently published CNNs methods.

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“…Additionally, it should reduce the computational cost and false-positive rate. Recently, significant advances have been observed in object detection using deep convolutional neural network (CNN) [ 20 , 22 – 24 ]. Convolutional neural networks (CNN) are a class of intensive, feed-forward artificial neural networks that have been used to perform accurately in computer vision tasks, such as image classification and detection.…”

Section: Literature Reviewmentioning

confidence: 99%

Monitoring social distancing through human detection for preventing/reducing COVID spread

Ansari

Singh

2021

Int. j. inf. tecnol.

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COVID-19 is a severe epidemic that has put the world in a global crisis. Over 42 Million people are infected, and 1.14 Million deaths are reported worldwide as on Oct 23, 2020. A deeper understanding of the epidemic suggests that a person’s negligence can cause widespread harm that would be difficult to negate. Since no vaccine is yet developed, social distancing must be practiced to detain COVID-19 spread. Therefore, we aim to develop a framework that tracks humans for monitoring the social distancing being practiced. To accomplish this objective of social distance monitoring, an algorithm is developed using object detection method. Here, CNN based object detector is explored to detect human presence. The object detector’s output is used for calculating distances between each pair of humans detected. This approach of social distancing algorithm will red mark the persons who are getting closer than a permissible limit. Experimental results prove that CNN based object detectors with our proposed social distancing algorithm exhibit promising outcomes for monitoring social distancing in public areas.

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Tracking People by Detection Using CNN Features

Cited by 45 publications

References 3 publications

$$\text{C}^{3}\text{Net}$$: end-to-end deep learning for efficient real-time visual active camera control

$$\text{C}^{3}\text{Net}$$: end-to-end deep learning for efficient real-time visual active camera control

Human Segmentation and Tracking Survey on Masks for MADS Dataset

Monitoring social distancing through human detection for preventing/reducing COVID spread

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