Ching-Chun Huang

Robust Parking Space Detection Considering Inter-Space Correlation

A major problem in metropolitan areas is searching for parking spaces. In this paper, we propose a novel method for parking space detection. Given input video captured by a camera, we can distinguish the empty spaces from the occupied spaces by using an 8-class support vector machine (SVM) classifier with probabilistic outputs. Considering the inter-space correlation, the outputs of the SVM classifier are fused together using a Markov random field (MRF) framework. The result is much improved detection performance, even when there are significant occlusion and shadowing effects in the scene. Experimental results are given to show the robustness of the proposed approach.

A Hierarchical Bayesian Generation Framework for Vacant Parking Space Detection

In this paper, from the viewpoint of scene under standing, a three-layer Bayesian hierarchical framework (BHF) is proposed for robust vacant parking space detection. In practice, the challenges of vacant parking space inference come from dramatic luminance variations, shadow effect, perspective distortion, and the inter-occlusion among vehicles. By using a hidden labeling layer between an observation layer and a scene layer, the BHF provides a systematic generative structure to model these variations. In the proposed BHF, the problem of luminance variations is treated as a color classification problem and is tack led via a classification process from the observation layer to the labeling layer, while the occlusion pattern, perspective distortion, and shadow effect are well modeled by the relationships between the scene layer and the labeling layer. With the BHF scheme, the detection of vacant parking spaces and the labeling of scene status are regarded as a unified Bayesian optimization problem subject to a shadow generation model, an occlusion generation model, and an object classification model. The system accuracy was evaluated by using outdoor parking lot videos captured from morning to evening. Experimental results showed that the proposed framework can systematically determine the vacant space number, efficiently label ground and car regions, precisely locate the shadowed regions, and effectively tackle the problem of luminance variations.

Motion-based Background Modeling for Moving Object Detection on Moving Platforms

A method to detect moving objects on non-stationary background is proposed. The concurrent motions of foreground and background pixels make it extremely difficult to maintain a plausible background model for background subtraction. In our method, motion fields of aligned neighboring frames are fused to reduce parallax effects in moving blob detection. A fused color background model is further developed to refine shapes of detected objects. Finally, moving blob information is incorporated into the adaptation process of background model. Only confidently marked background pixels are adapted into background models with each incoming frame. Experimental results shown robust, well-shaped moving object detection can be obtained under unconstrained scenes.

A Bayesian hierarchical detection framework for parking space detection

In this paper, a 3-layer Bayesian hierarchical detection framework (BHDF) is proposed for robust parking space detection. In practice, the challenges of the parking space detection problem come from luminance variations, inter- occlusions among cars, and occlusions caused by environmental obstacles. Instead of determining the status of parking spaces one by one, the proposed BHDF framework models the inter-occluded patterns as semantic knowledge and couple local classifiers with adjacency constraints to determine the status of parking spaces in a row-by-row manner. By applying the BHDF to the parking space detection problem, the available parking spaces and the labeling of parked cars can be achieved in a robust and efficient manner. Furthermore, this BHDF framework is generic enough to be used for various kinds of detection and segmentation applications.

Vacant Parking Space Detection Based on Plane-Based Bayesian Hierarchical Framework

In this paper, we propose a vacant parking space detection system that operates day and night. In the daytime, the major challenges of the system include dramatic lighting variations, shadow effect, inter-object occlusion, and perspective distortion. In the nighttime, the major challenges include insufficient illumination and complicated lighting conditions. To overcome these problems, we propose a plane-based method which adopts a structural 3-D parking lot model consisting of plentiful planar surfaces. The plane-based 3-D scene model plays a key part in handling inter-object occlusion and perspective distortion. On the other hand, to alleviate the interference of unpredictable lighting changes and shadows, we propose a plane-based classification process. Moreover, by introducing a Bayesian hierarchical framework to integrate the 3-D model with the plane-based classification process, we systematically infer the parking status. Last, to overcome the insufficient illumination in the nighttime, we also introduce a preprocessing step to enhance image quality. The experimental results show that the proposed framework can achieve robust detection of vacant parking spaces in both daytime and nighttime.

Dynamic Sampling Rate Adjustment for Compressive Spectrum Sensing over Cognitive Radio Network

In this paper, a dynamic sampling rate adjustment scheme is proposed for compressive spectrum sensing in cognitive radio network. Nowadays, compressive sensing (CS) has been proposed with a revolutionary idea to sense the sparse spectrum by using a lower sampling rate. However, many methods for compressive spectrum sensing assume that the sparse level is static and a fixed compressive sampling rate is applied over time. To adapt to time-varying sparse levels and adjust the sampling rate, we proposed to model sparse levels as a dynamic system and treat the dynamic rate selection as a tracking problem. By introducing the Sequential Monte Carlo (SMC) algorithm into a distributed compressive spectrum sensing framework, we could not only track the optimal sampling rate but determine the unoccupied channels accurately in a unified method.

Probabilistic Modeling of Dynamic Traffic Flow across Non-overlapping Camera Views

In this paper, we propose a probabilistic method to model the dynamic traffic flow across non-overlapping camera views. By assuming the transition time of object movement follows a certain global model, we may infer the time-varying traffic status in the unseen region without performing explicit object correspondence between camera views. In this paper, we model object correspondence and parameter estimation as a unified problem under the proposed Expectation-Maximization (EM) based framework. By treating object correspondence as a latent random variable, the proposed framework can iteratively search for the optimal model parameters with the implicit consideration of object correspondence.

RSS-Based Indoor Positioning Based on Multi-Dimensional Kernel Modeling and Weighted Average Tracking

In this paper, we discuss a similarity inconsistency phenomenon where the radio signal strength (RSS) signatures of two neighboring positions are dissimilar due to the RSS variation. While matching an observed RSS throughout the radio map, the phenomenon would lead to a jagged similarity distribution. This may break the similarity assumption of the previous works. To address the problem, we proposed a multi-dimensional kernel density estimation (MDKDE) method. By introducing the spatial kernel, the method could adopt neighboring information to enrich the fingerprint. The model can also help to generate a smooth and consistent similarity distribution. Moreover, we formulated the searching of the target location over the continuous domain as an optimization problem. Instead of estimating the optimal location numerically, we also came up with an efficient tracking method, weighted average tracker (WAT). Upon the MDKDE model, WAT can track the target in a simple weighted average method. The experimental results have demonstrated that the proposed system could well model the RSS variation and provide robust positioning performance in an efficient manner.

A surface-based vacant space detection for an intelligent parking lot

We proposed a surface-based vacant parking space detection system. Unlike many car-oriented or space-oriented methods, the proposed system is parking-lot-oriented. In the system, we treat the whole parking lot as a structure consisting of plentiful surfaces. A surface-based hierarchical framework is then proposed to integrate the 3-D scene information with the patch-based image observation for the inference of vacant space. To be robust, the feature vector of each image patch is extracted based on the Histogram of Oriented Gradients (HOG) approach. By incorporating these texture features into the proposed probabilistic models, we could systematically infer the optimal hypothesis of parking statuses while dealing with occlusion effect, shadow effect, perspective distortion, and fluctuation of lighting condition in both day time and night time.

Landmark-based device calibration and region-based modeling for RSS-based localization

During the past decades, many fingerprint-based indoor positioning systems have been proposed and have achieved great success. However, uncontrolled effects of device diversity, signal noise, and dynamic obstacles could recognizably degrade the performance of modern fingerprint-based indoor localization systems. In this paper, to amend the variations in radio signal strengths RSSs caused by device diversity, we proposed an automatic device calibration process. Because of device diversity, the sensed RSS would deviate from the trained radio map and thus leads to poor positioning. An RSS transform function could be adopted to calibrate the RSS variation between different devices and overcome the device diversity problem. However, to train the transform function, a data collection process is required. Unlike conventional calibration methods requiring manual data collection, we proposed a landmark-based automatic collection process. Based on the detection of Wi-Fi landmarks, our system could automatically collect pair-wise RSS samples between devices and train the RSS transform function without extra human power. In addition, to well represent the effects of signal noise and dynamic obstacles, a region-based RSS modeling method was also proposed. The proposed modeling method allows our system to perform region-based target localization and utilize more robust region information for localization. Experiments in various environments demonstrate that our system could give a better positioning performance by properly handling the RSS variation caused by signal noise, dynamic environment, and device diversity. Copyright