Heegwang Kim

Hybrid Sensor Network-Based Indoor Surveillance System for Intrusion Detection

This paper presents a novel hybrid sensor-based intrusion detection system for low-power surveillance in an empty, sealed indoor space with or without illumination. The proposed system includes three functional steps: (i) initial detection of an intrusion event using a sound field sensor; (ii) automatic lighting control based on the detected event, and (iii) detection and tracking the intruder using an image sensor. The proposed hybrid sensor-based surveillance system uses a sound field sensor to detect an abnormal event in a very low-light or completely dark environment for 24 h a day to reduce the power consumption. After detecting the intrusion by the sound sensor, a collaborative image sensor takes over an accurate detection and tracking tasks. The proposed hybrid system can be applied to various surveillance environments such as an office room after work, empty automobile, safety room in a bank, and armory room. This paper deals with fusion of computer-aided pattern recognition and physics-based sound field analysis that reflects the symmetric aspect of computer vision and physical analysis

Iterative refinement of transmission map for stereo image defogging

This paper presents a novel stereo image defogging algorithm using the disparity map. The proposed algorithm iteratively performs three steps; i) disparity map estimation using the optical flow, ii) transmission map generation using the estimated disparity map, and iii) reconstruction of defogged image using the transmission map. Experimental results show that the proposed method can successfully remove the fog without color distortion or ringing artifacts.

Iterative Refinement of Transmission Map for Stereo Image Defogging Using a Dual Camera Sensor

Recently, the stereo imaging-based image enhancement approach has attracted increasing attention in the field of video analysis. This paper presents a dual camera-based stereo image defogging algorithm. Optical flow is first estimated from the stereo foggy image pair, and the initial disparity map is generated from the estimated optical flow. Next, an initial transmission map is generated using the initial disparity map. Atmospheric light is then estimated using the color line theory. The defogged result is finally reconstructed using the estimated transmission map and atmospheric light. The proposed method can refine the transmission map iteratively. Experimental results show that the proposed method can successfully remove fog without color distortion. The proposed method can be used as a pre-processing step for an outdoor video analysis system and a high-end smartphone with a dual camera system.

Improved DCP-based image defogging using stereo images

Image defogging has recently received attentions in many applications such as advanced drive assistance systems (ADAS) and intelligent surveillance systems to acquire a high-quality images. This paper presents a novel depth-based image defogging method using stereo images. The depth information obtained from a pair of stereo foggy images and then fuzzy C-mean (FCM) clustering is applied to reduce matching errors caused by atmospheric absorption and scattering during light propagation. The estimated depth information is used as weighting values in the dark channel prior (DCP)-in the defogging process. Experimental results show that the proposed method can successfully remove foggy components in the image without color distortion.

Motion Estimation-based Human Fall Detection for Visual Surveillance

Currently, the world’s elderly population continues to grow at a dramatic rate. As the number of senior citizens increases, detection of someone falling has attracted increasing attention for visual surveillance systems. This paper presents a novel fall-detection algorithm using motion estimation and an integrated spatiotemporal energy map of the object region. The proposed method first extracts a human region using a background subtraction method. Next, we applied an optical flow algorithm to estimate motion vectors, and an energy map is generated by accumulating the detected human region for a certain period of time. We can then detect a fall using k-nearest neighbor (kNN) classification with the previously estimated motion information and energy map. The experimental results show that the proposed algorithm can effectively detect someone falling in any direction, including at an angle parallel to the camera’s optical axis.