This work investigates a semantic-driven human detection algorithm, which employs global human template matching to inspire the local features based Adaboosting algorithm. We use distance transform to analyze distances between training samples and human contour template to obtain a classifier based on human outline features. At the training stage, the global outline feature will be coordinated into the Adaboost framework to guide the learning of a set of HOGs local classifiers. In other words, we make a stronger human classifier by dynamically tuning the hyper-plane of the support vector machine to combine both global and local features for attaining a better accuracy. A global fused error rate is also proposed to enhance the modified Adaboost iterative calculation such that a human detector called strong classifier can be obtained. The experiments illustrate that the detection rate received from the presented framework is above 20% better than original HOGs-based human detector.

Changing lanes while having no information about the blind spot area can be dangerous. We propose a vision-based vehicle detection system for a lane changing assistance system to monitor the potential sedan vehicle in the blind-spot area. To serve our purpose, we select adequate features, which are directly obtained from vehicle images, to detect possible vehicles in the blind-spot area. This is challenging due to the significant change in the view angle of a vehicle along with its location throughout the blind-spot area. To cope with this problem, we propose a method to combine two kinds of part-based features that are related to the characteristics of the vehicle, and we build multiple models based on different viewpoints of a vehicle. The location information of each feature is incorporated to help construct the detector and estimate the reasonable position of the presence of the vehicle. The experiments show that our system is reliable in detecting various sedan vehicles in the blind-spot area.

It is desirable to know a resident's on-going activities before a robot or a smart system can provide attentive services to meet real human needs. This work addresses the problem of learning and recognizing human daily activities in a dynamic environment. Most currently available approaches learn offline activity models and recognize activities of interest on a real time basis. However, the activity models become outdated when human behaviors or device deployment have changed. It is a tedious and error-prone job to recollect data for retraining the activity models. In such a case, it is important to adapt the learnt activity models to the changes without much human supervision. In this work, we present a self-reconfigurable approach for activity recognition which reconfigures previously learnt activity models and infers multiple activities under a dynamic environment meanwhile pursuing minimal human efforts in relabeling training data by utilizing active-learning assistance.

This paper presents a probabilistic approach for automatically segmenting foreground objects from a video sequence. In order to save computation time and be robust to noise effects, a region detection algorithm incorporating edge information is first proposed to identify the regions of interest, within which the spatial relationships are represented by a region adjacency graph. Next, we consider the motion of the foreground objects and, hence, utilize the temporal coherence property in the regions detected. Thus, the foreground segmentation problem is formulated as follows. Given two consecutive image frames and the segmentation result priorly obtained, we simultaneously estimate the motion vector field and the foreground segmentation mask in a mutually supporting manner by maximizing the conditional joint probability density function of these two elements. To represent the conditional joint probability density function in a compact form, a Bayesian network is adopted, which is derived to model the interdependency of these two elements. Experimental results for several video sequences are provided to demonstrate the effectiveness of the proposed approach.

We introduce an Augmented Histograms of Oriented Gradients (AHOG) feature for human detection from a nonstatic camera. We increase the discriminating power of original Histograms of Oriented Gradients (HOG) feature by adding human shape properties, such as contour distances, symmetry, and gradient density. Based on the biological structure of human shape, we impose the symmetry property on HOG features by computing the similarity between itself and its’ symmetric pair to weight HOG features. After that, the capability of describing human features is much better than the original one, especially when the humans are moving across. We also augment the gradient density into features to mitigate the influences caused by repetitive backgrounds. In the experiments, our method demonstrates most reliable performance at any view of targets.

This paper presents a new approach to automatic segmentation of foreground objects from an image sequence by integrating techniques of background subtraction and motion-based foreground segmentation. First, a region-based motion segmentation algorithm is proposed to obtain a set of motion-coherence regions and the correspondence among regions at different time instants. Next, we formulate the classification problem as a graph labeling over a region adjacency graph based on Markov random fields (MRFs) statistical framework. A background model representing the background scene is built and then is used to model a likelihood energy. Besides the background model, a temporal coherence is also maintained by modeling it as the prior energy. On the other hand, color distributions of two neighboring regions are taken into consideration to impose spatial coherence. Then, the a priori energy of MRFs takes both spatial and temporal coherence into account to maintain the continuity of our segmentation. Finally, a labeling is obtained by maximizing the a posteriori energy of the MRFs. Under such formulation, we integrate two different kinds of techniques in an elegant way to make the foreground detection more accurate. Experimental results for several video sequences are provided to demonstrate the effectiveness of the proposed approach.

In this paper, the problem of human pose estimation is formulated as a multi-objective optimization problem so as to fuse multiple cues more properly, which is in contrast with the hypothesis that the cues are mutually independent so that their consolidation can be solely through the product of their individual likelihood distributions. An evolutionary algorithm for optimizing the defined objectives optimization is applied to evolve a set of non-dominated alternative solutions, known as the Pareto-optimal set. For convergence improvement of the evolutionary algorithm, the DD-MCMC method is used to generate a set of good initial solutions. Evaluating solutions by using relative dominant relation rather than quantitative absolute difference in value makes the solution exploration not dominated by the poor cue and result in more effective solutions for further decision making. Experimental results to the images obtained from different scene are provided to demonstrate the effectiveness and efficiency of our proposed framework

This paper presents a probabilistic approach for automatically segmenting foreground objects from a video sequence. A Bayesian network is presented to model the interactions among three variables, such as foreground segmentation mask, motion segmentation field, and motion vector held. Given two consecutive images, the conditional joint probability density of the three variables is maximized iteratively to simultaneously achieve foreground segmentation and motion segmentation in a mutually beneficial manner. The solution to the optimization problems are obtained by using iterative conditional mode (ICM) and graph cut algorithm. Incorporating motion information with background subtraction technique makes the segmentation perform in a more semantic level and obtain more accurate results. Experimental results for two video sequences are provided to demonstrate the effectiveness of the proposed approach.

This paper presents a new approach to automatic segmentation of the foreground objects from the sequence of images by integrating techniques of background subtraction and motion-based segmentation. At first, a background model is built to represent information of both color and motion of the background scene. Based on temporal and spatial information, an initial partition of each image is obtained. Next, we formulate the classification problem as a graph labeling over a region adjacency graph (RAG) based on Markov random fields (MRFs) statistical framework. The Bhattacharyya distance for estimating the similarity between color and motion distributions of the background model and the currently obtained regions are used to model the likelihood energies. The object tracking strategy for finding the correspondence between region at different time instant is used to maintain the temporal coherence of the segmentation. For spatial coherence, the length of the common boundaries of two regions is taken into consideration for classification. Both spatial and temporal coherence are incorporated into the prior energy to maintain the continuity of the segmentation. Finally, a labeling is obtained by maximizing a posterior probability of the MRFs. Under such formulation, we integrate two different kinds of framework in an elegant way to make the foreground detection become more accurate. Experimental results for two image sequences including the hall monitoring and our e-home demo site are provided to demonstrate the effectiveness of the proposed approach.

The objective of this research is to develop a vision-based driver assistance system to enhance the driver's safety in the nighttime. The proposed system performs both lane detection and vehicle recognition. In lane detection, three features including lane markers, brightness, slenderness and proximity are applied to detect the positions of lane markers in the image. On the other hand, vehicle recognition is achieved by using an evident feature which is extracted through three four steps: taillight standing-out process, adaptive thresholding, centroid detection, and taillight pairing algorithm. Besides, an automatic method is also provided to calculate the tilt and the pan of the camera by using the position of vanishing point which is detected in the image by applying Canny edge detection, Hough transform, major straight line extraction and vanishing point estimation. Experimental results for thousands of images are provided to demonstrate the effectiveness of the proposed approach in the nighttime. The lane detection rate is nearly 99%, and the vehicle recognition rate is about 91%. Furthermore, our system can process the image in almost real time.

The objectives of this research are to develop a driving assistance system that can locate the positions of the lane boundaries and detect the existence of the front-vehicle. By providing warning mechanism, the system can protect drivers from dangerousness. In lane recognition, Gaussian filter, peak-finding procedure, and line-segment grouping procedure are used to detect land markers successfully and effectively. On the other hand, vehicle detection is achieved by using three features, such as underneath, vertical edge, and symmetry property. The proposed system is shown to work well under various conditions on the roadway. The vehicle detection rate is higher than 97%. Besides, the computation cost is inexpensive and the system's response is almost real time. Thus, the results of the present research work can improve traffic safety for on-road driving.

This work presents a region-based approach for background modeling to economize the use of space. In order to be immune to noise and changes resulting from illumination or motion, the partial directed Hausdorff distance is adopted while subtracting the foreground objects from the scene robustly. Instead of determining the threshold values manually, we use an adaptive method to automatically choose the threshold values. Finally, experimental results of applying our approach on a sequence of an indoor scene are provided to demonstrate the effectiveness of the proposed method.

Autonomous driving system can assist to prevent the traffic accidents caused by the negligence of the driver. Obstacle detection and warning mechanism plays an important role in this research field. In this paper, we adopt the computer vision technology because of its large detecting range and abundant information when compared to other kinds of sensors. The proposed system is suitable for both the simplified environment such as freeway and the urban environment with complex background. Thus, the result here improves traffic safety not only for drivers, but also for all pedestrians on the road.

Applying computer technology to vehicle driving has been studied for many years. In this research field, obstacle detection plays an important role in assisting drivers with warning mechanism when some dangerous situations may happen. In this paper, we propose a fast method for detecting and tracking bikes, pedestrians, and vehicles in front of a moving vehicle. In order to detect bikes and pedestrians efficiently, we apply a simplified fast stereo vision method to estimate their approximate positions. On the other hand, we apply the so-called sign pattern technique to estimate the vehicle positions. After that, different methods are used to classify and confirm different kinds of obstacles for adapting their heterogeneity.

In the past years, buildings are designed to be more and more powerful and intelligent mechanism, but it is still far from being perfect since building systems are lack of a uniform specification to establish standards for various appliances and communication protocol, and are also lack of a robust kernel to integrate all sub-systems in the buildings. For the former problem, we expect that the major appliance producers will eventually set up the standard for the appliances and communication protocol. For the latter problem, this paper proposes a complete system architecture with integrated control kernel to construct an intelligent building system rapidly and efficiently.

Wafer fabrication is one of the most competitive manufacturing business in the world. In order to survive in such a strongly competitive environment, finding an effective schedule which can result in higher machine utilization and throughput rate, shorter cycle time, and lower WIP (work-in-process) inventory becomes a major task. Besides that, in order to help customers to make ordering decisions as well as to let the manager control the processing conditions of the fab, we need to predict some performance measures efficiently. We propose a modeling tool called queueing-Petri net (Q-PN) which combines the characteristics of queueing theory and Petri nets. It can be used to model various details of the manufacturing systems as well as to evaluate its performance very efficiently. Then, a general Q-PN model is presented to simulate the semiconductor manufacturing system. Based on this model, we propose a genetic algorithm (GA) based scheduler and an analysis-based predictor. In the GA scheduler, the chromosome represents a combination of scheduling policies, including lot release policies, machine selection rules, dispatch rules and batch rules. So, when the GA finishes its optimization process, an optimal scheduling policy is produced. As for the predictor, because it inherits the analytical property of queueing theory from the Q-PN model, we can use it to predict those performance measures efficiently such as the exact due date of some particular lot.