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In the demonstration, we would show our live and real-time parking space detection system. The detection function is founded on a video surveillance system built in an outdoor parking lot. As we might know, it is challenging to implement a practical vision system in an outdoor environment owing to the dramatic lighting changes and uncontrollable variations from weather conditions.

Vacant parking space detection is a challenging vision task due to outdoor lighting variation and perspective distortion. Previous methods found on camera geometry and projection matrix to select space image region for status classification. By utilizing suitable hand-crafted features, outdoor lighting variation and perspective distortion could be well handled. However, if also considering parking displacement, non-unified car size, and inter-object occlusion, we find the problem becomes more troublesome.

The problem of human activity recognition can be approached using spatio-temporal variations in successive video frames. In this paper, a new human action recognition technique is proposed using multi-view videos. Initially, a naive background subtraction using frame differencing between adjacent frames of a video is performed. Then, the motion information of each pixel is recorded in binary indicating existence/nonexistence of motion in the frame. A pixel wise sum over all the difference images in a view gives the frequency of motion in each pixel throughout the clip.

In this paper, we present a novel fully automatic approach to track the same human across multiple disjoint cameras. Our framework includes a two-phase feature extractor and an online-learning-based camera link model estimation. We introduce an effective and robust integration of appearance and context features. Couples are detected automatically, and the couple feature is also integrated with appearance features effectively. The proposed algorithm is scalable with the use of a fully unsupervised online learning framework.

High accuracy is required for determining the orientation of a satellite in space. Amongst the existing sensors, a star tracker provides a very high accuracy of attitude determination. When no prior attitude is available, it operates in the “Lost-In-Space (LIS)” mode. Star pattern recognition is the most crucial part of a star tracker in the LIS mode. In this paper, a novel star pattern recognition approach is proposed, which constructs a signal from the features extracted in the star image and utilizes spearman correlation for identifying the correct stars.

Heavy fog degrades the quality of road images in losing contrast and color fidelity, which may cause errors in stereo matching and road segmentation for advanced driver assistance systems (ADAS). Accident rates can be reduced if robust and efficient algorithms are applied for fog removal. Most studies have been conducted on this subject to date, but existing methods are not suitable for heavy haze conditions. Local darkness, under-estimation and over enhancement are always occurred after dehazing.

Heavy fog degrades the quality of road images in losing contrast and color fidelity, which may cause errors in stereo matching and road segmentation for advanced driver assistance systems (ADAS). Accident rates can be reduced if robust and efficient algorithms are applied for fog removal. Most studies have been conducted on this subject to date, but existing methods are not suitable for heavy haze conditions. Local darkness, under-estimation and over enhancement are always occurred after dehazing.

Heavy fog degrades the quality of road images in losing contrast and color fidelity, which may cause errors in stereo matching and road segmentation for advanced driver assistance systems (ADAS). Accident rates can be reduced if robust and efficient algorithms are applied for fog removal. Most studies have been conducted on this subject to date, but existing methods are not suitable for heavy haze conditions. Local darkness, under-estimation and over enhancement are always occurred after dehazing.