CamLoc: Pedestrian Location Detection from Pose Estimation on Resource-constrained Smart-cameras

12/28/2018
by   Adrian Cosma, et al.
6

Recent advancements in energy-efficient hardware technology is driving the exponential growth we are experiencing in the Internet of Things (IoT) space, with more pervasive computations being performed near to data generation sources. A range of intelligent devices and applications performing local detection is emerging (activity recognition, fitness monitoring, etc.) bringing with them obvious advantages such as reducing detection latency for improved interaction with devices and safeguarding user data by not leaving the device. Video processing holds utility for many emerging applications and data labelling in the IoT space. However, performing this video processing with deep neural networks at the edge of the Internet is not trivial. In this paper we show that pedestrian location estimation using deep neural networks is achievable on fixed cameras with limited compute resources. Our approach uses pose estimation from key body points detection to extend pedestrian skeleton when whole body not in image (occluded by obstacles or partially outside of frame), which achieves better location estimation performance (infrence time and memory footprint) compared to fitting a bounding box over pedestrian and scaling. We collect a sizable dataset comprising of over 2100 frames in videos from one and two surveillance cameras pointing from different angles at the scene, and annotate each frame with the exact position of person in image, in 42 different scenarios of activity and occlusion. We compare our pose estimation based location detection with a popular detection algorithm, YOLOv2, for overlapping bounding-box generation, our solution achieving faster inference time (15x speedup) at half the memory footprint, within resource capabilities on embedded devices, which demonstrate that CamLoc is an efficient solution for location estimation in videos on smart-cameras.

READ FULL TEXT

page 1

page 4

page 5

page 7

page 8

research
01/13/2020

Deep Learning based Pedestrian Inertial Navigation: Methods, Dataset and On-Device Inference

Modern inertial measurements units (IMUs) are small, cheap, energy effic...
research
07/27/2020

YOLOpeds: Efficient Real-Time Single-Shot Pedestrian Detection for Smart Camera Applications

Deep Learning-based object detectors can enhance the capabilities of sma...
research
11/24/2019

Fatigue Detection

Nowadays, there are many fatigue detection methods and the majority of t...
research
06/06/2019

Smart IoT Cameras for Crowd Analysis based on augmentation for automatic pedestrian detection, simulation and annotation

Smart video sensors for applications related to surveillance and securit...
research
09/12/2019

Efficient 2.5D Hand Pose Estimation via Auxiliary Multi-Task Training for Embedded Devices

2D Key-point estimation is an important precursor to 3D pose estimation ...
research
04/01/2023

Pedestrian Intention Classifier using ID3 Modelled Decision Trees for IoT Edge Devices

Road accidents involving autonomous vehicles commonly occur in situation...
research
09/11/2020

Enabling Image Recognition on Constrained Devices Using Neural Network Pruning and a CycleGAN

Smart cameras are increasingly used in surveillance solutions in public ...

Please sign up or login with your details

Forgot password? Click here to reset