Low-power object counting with hierarchical neural networks

Goel, Abhinav; Tung, Caleb; Aghajanzadeh, Sara; Ghodgaonkar, Isha; Ghosh, Shreya; Thiruvathukal, George K.; Lu, Yung-Hsiang

doi:10.1145/3370748.3406569

Cited by 9 publications

(2 citation statements)

References 27 publications

(48 reference statements)

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“…Detection methods include You Only Look Once (YOLO) [38] and Single-Shot-Detector (SSD) [21]. Object counting may use hierarchical object counting [10]. These early methods are inaccurate with estimating density in moderate to dense crowds.…”

Section: A Detecting People and Vehicles In Imagesmentioning

confidence: 99%

Analyzing Worldwide Social Distancing through Large-Scale Computer Vision

Ghodgaonkar,

Chakraborty,

Banna

et al. 2020

Preprint

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In order to contain the COVID-19 pandemic, countries around the world have introduced social distancing guidelines as public health interventions to reduce the spread of the disease. However, monitoring the efficacy of these guidelines at a large scale (nationwide or worldwide) is difficult. To make matters worse, traditional observational methods such as inperson reporting is dangerous because observers may risk infection. A better solution is to observe activities through network cameras; this approach is scalable and observers can stay in safe locations. This research team has created methods that can discover thousands of network cameras worldwide, retrieve data from the cameras, analyze the data, and report the sizes of crowds as different countries issued and lifted restrictions (also called "lockdown"). We discover 11,140 network cameras that provide real-time data and we present the results across 15 countries. We collect data from these cameras beginning April 2020 at approximately 0.5TB per week. After analyzing 10,424,459 images from still image cameras and frames extracted periodically from video, the data reveals that the residents in some countries exhibited more activity (judged by numbers of people and vehicles) after the restrictions were lifted. In other countries, the amounts of activities showed no obvious changes during the restrictions and after the restrictions were lifted. The data further reveals whether people stay "social distancing", at least 6 feet apart. This study discerns whether social distancing is being followed in several types of locations and geographical locations worldwide and serve as an early indicator whether another wave of infections is likely to occur soon.

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Section: A Detecting People and Vehicles In Imagesmentioning

confidence: 99%

Analyzing Worldwide Social Distancing through Large-Scale Computer Vision

Ghodgaonkar,

Chakraborty,

Banna

et al. 2020

Preprint

Self Cite

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“…Another effective approach is to use hierarchical networks [10]. Those networks have shown success in image classification tasks because each image has only one object, so the objects can be grouped based on their visual or semantic similarities [11]. However, for object detection, an image can have objects that are not visually or semantically similar.…”

Section: Introductionmentioning

confidence: 99%

AdaCon: Adaptive Context-Aware Object Detection for Resource-Constrained Embedded Devices

Neseem¹,

Reda²

2021

Preprint

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Convolutional Neural Networks achieve state-of-theart accuracy in object detection tasks. However, they have large computational and energy requirements that challenge their deployment on resource-constrained edge devices. Object detection takes an image as an input, and identifies the existing object classes as well as their locations in the image. In this paper, we leverage the prior knowledge about the probabilities that different object categories can occur jointly to increase the efficiency of object detection models. In particular, our technique clusters the object categories based on their spatial co-occurrence probability. We use those clusters to design an adaptive network. During runtime, a branch controller decides which part(s) of the network to execute based on the spatial context of the input frame. Our experiments using COCO dataset show that our adaptive object detection model achieves up to 45% reduction in the energy consumption, and up to 27% reduction in the latency, with a small loss in the average precision (AP) of object detection.

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Efficient Computer Vision on Edge Devices with Pipeline-Parallel Hierarchical Neural Networks

Goel

Tung

et al. 2022

2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC)

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Low-power object counting with hierarchical neural networks

Cited by 9 publications

References 27 publications

Analyzing Worldwide Social Distancing through Large-Scale Computer Vision

Analyzing Worldwide Social Distancing through Large-Scale Computer Vision

AdaCon: Adaptive Context-Aware Object Detection for Resource-Constrained Embedded Devices

Efficient Computer Vision on Edge Devices with Pipeline-Parallel Hierarchical Neural Networks

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