[서평]「Digital Video Processing」

Medical image encryption using high-speed scrambling and pixel adaptive diffusion

Recent survey on crowd density estimation and counting for visual surveillance

With the proliferation of Internet of Things (IoT) devices such as smartphones, sensors, cameras, and RFIDs, it is possible to collect massive amount of data for localization and tracking of people within commercial buildings. Enabled by such occupancy monitoring capabilities, there are extensive opportunities for improving the energy consumption of buildings via smart HVAC control. In this respect, the major challenges we envision are 1) to achieve occupancy monitoring in a minimally intrusive way, e.g., using the existing infrastructure in the buildings and not requiring installation of any apps in the users’ smart devices, and 2) to develop effective data fusion techniques for improving occupancy monitoring accuracy using a multitude of sources. This paper surveys the existing works on occupancy monitoring and multi-modal data fusion techniques for smart commercial buildings. The goal is to lay down a framework for future research to exploit the spatio-temporal data obtained from one or more of various IoT devices such as temperature sensors, surveillance cameras, and RFID tags that may be already in use in the buildings. A comparative analysis of existing approaches and future predictions for research challenges are also provided.

IoT-based occupancy monitoring techniques for energy-efficient smart buildings

A review of multimodal image matching: Methods and applications

Moving object detection and tracking from video captured by moving camera

This paper presents an automatic people counting system for getting in/out of a bus based on video processing. The basic scheme is to set a zenithal camera in the bus for capturing the passenger flow bi-directionally. The captured frame is firstly divided into many blocks and each block will be classified according to its motion vector. If the block quantity of similar motion vectors is more than a threshold, those blocks are regarded as belonging to the same moving object. As a result, the number of such moving objects is counted to be the passenger number of getting in or out of a bus. can be segmented for counting. Experimental results show that the proposed bus-passenger counting algorithm can provide a high count accuracy of 92% on average.

People Counting System for Getting In/Out of a Bus Based on Video Processing

Weighted haze removal method with halo prevention

This paper presents an automatic people counting system based on face detection, where the number of people passing through a gate or door is counted by setting a video camera. The basic idea is to first use the frame difference to detect the rough edges of moving people and then use the chromatic feature to locate the people face. Based on NCC (Normalized Color Coordinates) color space, the initial face candidate is obtained by detecting the skin color region and then the face feature of the candidate is analyzed to determine whether the candidate is real face or not. After face detection, a person will be tracked by following the detected face and then this person will counted if its face touches the counting line. Experimental results show that the proposed people counting algorithm can provide a high count accuracy of 80% on average for the crowded pedestrians.

Chao-Ho Chen

Papers

Moving object detection and tracking from video captured by moving camera

People Counting System for Getting In/Out of a Bus Based on Video Processing

Weighted haze removal method with halo prevention

A People Counting System Based on Face-Detection

Vehicle detection and inter-vehicle distance estimation using single-lens video camera on urban/suburb roads