Person Detection from Overhead View: A Survey

Ahmad, Misbah; Ahmed, Imran; Ullah, Kaleem; Khan, Iqbal; Khattak, Ayesha; Adnan, Awais

doi:10.14569/ijacsa.2019.0100470

Cited by 26 publications

(13 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…FCN [17], U-Net [21] and DeepLabV3 framework [22] have been explored for top view person segmentation. The top view perspective offers wide coverage, better visibility of the objects in the field of view, and may handle occlusion problems better than frontal view [23] and [24]. Moreover, using a single top view camera is also beneficial in terms of installation expense, energy usage, and human resource (required to monitor multiple cameras) [25].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Deep-Learning-Based Segmentation Models: Using Top View Person Images

2020

Self Cite

View full text Add to dashboard Cite

Image segmentation is considered as a key research topic in the area of computer vision. It is pivotal in a broad range of real-life applications. Recently, the emergence of deep learning drives significant advancement in image segmentation; the developed systems are now capable of recognizing, segmenting, and classifying objects of specific interest in images. Generally, most of these techniques primarily focused on the asymmetric field of view or frontal view objects. This work explores widely used deep learningbased models for person segmentation using top view data set. The first model employed in this work is Fully Convolutional Neural Network (FCN) with Resnet-101 architecture. The network consists of a set of max-pooling and convolution layers to identify pixel-wise class labels and prediction of the mask. The second model is based on FCN called U-Net with Encoder-Decoder architecture. The encoder is mainly comprised of a contracting path, also called an encoder, which captures the context in the image and symmetric expanding path called decoder to enable accurate location. The third model used for top view person segmentation is a DeepLabV3 model also with encoder-decoder architecture. The encoder consists of trained Convolutional Neural Network (CNN) to encode feature maps of the input image. The decoder is used for up-sampling and reconstruction of output using important information extracted by the encoder. All segmentation models are firstly tested using pre-trained models (trained on frontal view data set). To improve the performance, these models are further trained using person data set captured from a top view. The output of all models consists of a segmented person in the top view images. The experimental results reveal the effectiveness and performance of segmentation models by achieving IoU of 83%, 84%, and 86% and mIoU of 80% 82%and 84% for FCN, U-Net, and DeepLabv3 respectively. Furthermore, the discussion is provided for output results with possible future guidelines.

show abstract

Section: Introductionmentioning

confidence: 99%

Comparison of Deep-Learning-Based Segmentation Models: Using Top View Person Images

2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Compared to RGB-based-only methods, the combination of top-view and RGB-depth cameras provides more straightforward solutions for human detection [ 27 ]. The top-view image offers an occlusion-free view as the occlusion rarely occurs in the top-view direction, which takes images from directly above.…”

Section: Related Workmentioning

confidence: 99%

A Hierarchy-Based System for Recognizing Customer Activity in Retail Environments

Wen

Guillen

Abe

et al. 2021

Sensors

View full text Add to dashboard Cite

Customer activity (CA) in retail environments, which ranges over various shopper situations in store spaces, provides valuable information for store management and marketing planning. Several systems have been proposed for customer activity recognition (CAR) from in-store camera videos, and most of them use machine learning based end-to-end (E2E) CAR models, due to their remarkable performance. Usually, such E2E models are trained for target conditions (i.e., particular CA types in specific store spaces). Accordingly, the existing systems are not malleable to fit the changes in target conditions because they require entire retraining of their specialized E2E models and concurrent use of additional E2E models for new target conditions. This paper proposes a novel CAR system based on a hierarchy that organizes CA types into different levels of abstraction from lowest to highest. The proposed system consists of multiple CAR models, each of which performs CAR tasks that belong to a certain level of the hierarchy on the lower level’s output, and thus conducts CAR for videos through the models level by level. Since these models are separated, this system can deal efficiently with the changes in target conditions by modifying some models individually. Experimental results show the effectiveness of the proposed system in adapting to different target conditions.

show abstract

“…A comprehensive survey of different tracking methods can be found in previous studies. 1,13,14 The key purpose of tracking techniques are to detect objects in a video sequence and sustain the tracking information in the successive frames to find trajectories of each detected object. Conventional techniques are usually based on motion-and observation-based models.…”

Section: Literature Reviewmentioning

confidence: 99%

Convolutional neural network–based person tracking using overhead views

Ahmad

Khan

Qayum

et al. 2020

International Journal of Distributed Sensor Networks

Self Cite

View full text Add to dashboard Cite

In video surveillance, person tracking is considered as challenging task. Numerous computer vision, machine and deep learning–based techniques have been developed in recent years. Majority of these techniques are based on frontal view images/video sequences. The advancement of convolutional neural network reforms the way of object tracking. The network layers of convolutional neural network models trained on a number of images or video sequences improve speed and accuracy of object tracking. In this work, the generalization performance of existing pre-trained deep learning models have investigated for overhead view person detection and tracking, under different experimental conditions. The object tracking method Generic Object Tracking Using Regression Networks (GOTURN) which has been yielding outstanding tracking results in recent years is explored for person tracking using overhead views. This work mainly focused on overhead view person tracking using Faster region convolutional neural network (Faster-RCNN) in combination with GOTURN architecture. In this way, the person is first identified in overhead view video sequences and then tracked using a GOTURN tracking algorithm. Faster-RCNN detection model achieved the true detection rate ranging from 90% to 93% with a minimum false detection rate up to 0.5%. The GOTURN tracking algorithm achieved similar results with the success rate ranging from 90% to 94%. Finally, the discussion is made on output results along with future direction.

show abstract

Person Detection from Overhead View: A Survey

Cited by 26 publications

References 27 publications

Comparison of Deep-Learning-Based Segmentation Models: Using Top View Person Images

Comparison of Deep-Learning-Based Segmentation Models: Using Top View Person Images

A Hierarchy-Based System for Recognizing Customer Activity in Retail Environments

Convolutional neural network–based person tracking using overhead views

Contact Info

Product

Resources

About