Taeyup Song

License Plate Detection Using Local Structure Patterns

We address the problem of license plate detection invideo surveillance systems. The Adaboost based approach,known for relative ease of implementation, makes use ofdiscriminative features such as edges or Haar-like features.In this paper, we propose a novel detection algorithm basedon local structure patterns for license plate detection. Theproposed algorithm includes post-processing methods toreduce false positive rate using positional and colorinformation of license plates. Experimental resultsdemonstrate effectiveness of the proposed methodcompared

Online multi-person tracking for intelligent video surveillance systems

This paper presents a novel online multi-person tracking method based on tracking-by-detection framework for intelligent video surveillance systems consisting of an internet protocol (IP) camera and a network video recorder (NVR). First, the two-step data association based on high/low confidence targets is proposed for handling long-term occlusions effectively. Additionally, the method that reasons and handles severe inter-target occlusion is proposed. Representative experimental results demonstrate the effectiveness and robustness of the proposed method.

Visual voice activity detection via chaos based lip motion measure robust under illumination changes

In this paper, a vision based voice activity detection (VVAD) algorithm is proposed using chaos theory. In conventional VVAD algorithm, the movement measure of lip region is found by applying an optical flow algorithm to detect the visual speech frame using a motion based energy feature set. However, since motion based feature is unstable under illumination changes, a new form of robust feature set is desirable. It is propositioned that contextual changes such as lip opening or closing motion during speech utterances under illumination variation can be observed as chaos-like and the resultant complex fractal trajectories in phase space can be observed. The fractality is measured in phase space from two sequential video input frames and subsequently any visual speech frames are robustly detected. Representative experiments are performed in image sequence containing a driver scene undergoing illumination fluctuations in moving vehicle environment. Experimental results indicate that a substantial improvement is obtained in terms of achieving significantly lower false alarm rate over the conventional method.

Robust visual voice activity detection using chaos theory under illumination varying environment

We propose a visual based voice activity detection robust in illumination changes which frequently occurs in indoor vehicle environment. By applying chaos theory, contextual change becomes chaos-like and results in complex fractal trajectories in phase space. We measure fractality in phase space from image frames and thereby robustly detect visual speech motions under illumination changes. Representative experiments demonstrate significant improvements over conventional method.

Effective character segmentation for license plate recognition under illumination changing environment

In this paper, we propose a novel image segmentation algorithm for license plate recognition (LPR) in video based traffic surveillance system. The license plate character segmentation is most important procedure in LPR system. However, in real situation, the character segmentation algorithms are challenged by drastic performance decrease due to sudden local illumination changes, especially when the color of characters is similar to that of background in LP. To mitigate this problem, we introduce a novel LP character segmentation algorithm by employing an adaptive binarization method using super-pixel based degeneracy factor. The proposed method demonstrates a significant improvement over conventional methods.

Robust background subtraction using data fusion for real elevator scene

This paper proposes a background subtraction technique robust in elevator environments. Sudden local illumination changes arise frequently in an elevator environment due to opening and closing of the elevator door as well as the inner walls of elevator being made of reflective materials. We present a novel method sequentially fusing a Gaussian mixture model for background subtraction, motion information and a spatial likelihood model based on textured features. Experimental results on real video data demonstrate effectiveness of the proposed approach.

Robust visual voice activity detection using local variance histogram in vehicular environments

In this paper, a Vision based Voice Activity Detection (VVAD) algorithm is proposed using Local Variance Histogram (LVH). In conventional VVAD algorithm, the motion measure such as optical flow and intensity histogram are widely used. However, this approach is unstable under varying illumination and global motion changes which frequently occur in moving vehicular environment. To mitigate this problem, an appropriate framework based on LVH feature is developed. Comparison with two other conventional visual voice activity detectors shows the proposed method to be consistently more accurate and yields a substantial improvement in terms of detection probability and false alarm rate.

Visual Voice Activity Detection and Adaptive Threshold Estimation for Speech Recognition

In this paper, we propose an algorithm for achieving robust Visual Voice Activity Detection (VVAD) for enhanced speech recognition. In conventional VVAD algorithms, the motion of lip region is found by applying an optical flow or Chaos inspired measures for detecting visual speech frames. The optical flow-based VVAD is difficult to be adopted to driving scenarios due to its computational complexity. While invariant to illumination changes, Chaos theory based VVAD method is sensitive to motion translations caused by driver`s head movements. The proposed Local Variance Histogram (LVH) is robust to the pixel intensity changes from both illumination change and translation change. Hence, for improved performance in environmental changes, we adopt the novel threshold estimation using total variance change. In the experimental results, the proposed VVAD algorithm achieves robustness in various driving situations.