Real-Time Top-View People Counting Based on a Kinect and NVIDIA Jetson TK1 Integrated Platform

In this paper, we describe how to establish an embedded framework for real-time top-view people counting. The development of our system consists of two parts, i.e. establishing an embedded signal processing platform and designing a people counting algorithm for the embedded system. For the hardware platform construction, we use Kinect as the camera and exploit NVIDIA Jetson TK1 board as the embedded processing platform. We describe how to build a channel to make Kinect for windows version 2.0 communicate with Jetson TK1. Based on the embedded system, we adapt a water filling based scheme for top-view people counting, which integrates head detection based on water drop, people tracking and counting. Gaussian Mixture Model is used to construct and update the background model. The moving people in each video frame are extracted using background subtraction method. Additionally, the water filling algorithm is used to segment head area as Region Of Interest(ROI). Tracking and counting people are performed by calculating the distance of ROI center point before and after the frame. The whole framework is flexible and practical for real-time application.

[1]  Ben J. A. Kröse,et al.  Head Detection in Stereo Data for People Counting and Segmentation , 2011, VISAPP.

[2]  Jana Abhijit.  Kinect for Windows SDK Programming Guide , 2012 .

[3]  José Luis Lázaro,et al.  Embedded Vision Modules for Tracking and Counting People , 2009, IEEE Transactions on Instrumentation and Measurement.

[4]  Luigi di Stefano,et al.  People Tracking Using a Time-of-Flight Depth Sensor , 2006, 2006 IEEE International Conference on Video and Signal Based Surveillance.

[5]  Junjie Yan,et al.  Water Filling: Unsupervised People Counting via Vertical Kinect Sensor , 2012, 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance.

[6]  Ya-Ching Chang,et al.  Real-time People Counting Method with Surveillance Cameras Implemented on Embedded System , 2013 .

[7]  Tsong-Yi Chen,et al.  An Intelligent People-Flow Counting Method for Passing Through a Gate , 2006, 2006 IEEE Conference on Robotics, Automation and Mechatronics.

[8]  Meng Wang,et al.  Multimodal Deep Autoencoder for Human Pose Recovery , 2015, IEEE Transactions on Image Processing.

[9]  Emmanuel Dellandréa,et al.  A People Counting System Based on Face Detection and Tracking in a Video , 2009, 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance.

[10]  Nuno Vasconcelos,et al.  Privacy preserving crowd monitoring: Counting people without people models or tracking , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Nikom Suvonvorn,et al.  Top-view Based People Counting Using Mixture of Depth and Color Information , 2013 .

[12]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[13]  Pietro Cerri,et al.  An embedded system for counting passengers in public transportation vehicles , 2014, 2014 IEEE/ASME 10th International Conference on Mechatronic and Embedded Systems and Applications (MESA).

[14]  W. Marsden.  I and J , 2012 .

[15]  Meng Wang,et al.  Image-Based Three-Dimensional Human Pose Recovery by Multiview Locality-Sensitive Sparse Retrieval , 2015, IEEE Transactions on Industrial Electronics.