Pei Lv

Novel Applications of VR: Virtual Network Marathon with immersion, scientificalness, competitiveness, adaptability and learning

In this paper, we present an exergame called VNM (Virtual Network Marathon). The VNM employs specially devised treadmills for running in an immersive virtual environment on a local area network or the Internet. The treadmills have various sensors embedded within them for collecting body performance data. These treadmills are then connected with computers or Set-Top-Boxes (STBs) to synchronously control the players avatar in a virtual game environment. VNM is implemented on a novel ISCAL (Immersion, Scientificalness, Competitiveness, Adaptability and Learning) model for exergame design. The exercise time and intensity strictly conform to the physical exercise guidelines of the ACSM (American College of Sports Medicine), and a novel demonstration-based non-player modeling technique is employed to simulate a marathon race. The VNM also allows players to learn Chinese culture and obtain knowledge of the Olympics while in tour mode during or after the running exercise. We conducted a pilot study to evaluate the usability of the features of the VNM that we have developed, and with promising results.

Robust Lane Detection using Two-stage Feature Extraction with Curve Fitting

With the increase in the number of vehicles, many intelligent systems have been developed to help drivers to drive safely. Lane detection is a crucial element of any driver assistance system. At present, researchers working on lane detection are confronted with several major challenges, such as attaining robustness to inconsistencies in lighting and background clutter. To address these issues in this work, we propose a method named Lane Detection with Two-stage Feature Extraction (LDTFE) to detect lanes, whereby each lane has two boundaries. To enhance robustness, we take lane boundary as collection of small line segments. In our approach, we apply a modified HT (Hough Transform) to extract small line segments of the lane contour, which are then divided into clusters by using the DBSCAN (Density Based Spatial Clustering of Applications with Noise) clustering algorithm. Then, we can identify the lanes by curve fitting. The experimental results demonstrate that our modified HT works better for LDTFE than LSD (Line Segment Detector). Through extensive experiments, we demonstrate the outstanding performance of our method on the challenging dataset of road images compared with state-of-the-art lane-detection methods. HighlightsWe proposed a novel lane detection method.Our method regards lane boundary as collection of small line segments.We proposed a modified Hough Transform to detect small line segments.Small line segments are clustered based on our proposed similarity measurement.Removing interferential clusters depends on the balance of small line segments.

miSFM: On combination of mutual information and social force model towards simulating crowd evacuation

Abstract In this paper we propose a novel technique termed miSFM for the simulation of crowd evacuation. miSFM take merits of both Mutual Information (MI) and Social Force Model (SFM). More specifically, MI of interacting agents is adopted to determine the level of order within a crowd during an evacuation. In such a way, SFM can be improved by adapting the forces involved at microscopic level between mutually interacting agents. The key innovation lies in highlighting how the dynamic adjustment of SFM parameters reveals much more realistic crowd movements for the evacuation simulation. Extensive experiments over several alternative and state-of-the-art works demonstrate the advantages of the proposed algorithm.

Biomechanics-based reaching optimization

This paper presents a Biomechanics-based Lifelike Reaching Controller (BLRC) to generate lifelike reaching motion. The BLRC employs various reaching strategies borrowed from biomechanics to guarantee the naturalness of reaching motion and expands the reachable space to enrich the flexibility of human behavior. We exploit the arm-reachable workspace to guide the motion sampling, and construct different low-dimensional space for each reaching strategy by PCA to reduce the search space, so as to make BLRC fast deal with huge mocap data set. Moreover, we also use the optimization method in these low-dimensional spaces to further speed up the convergence of motion synthesis with the help of the accurate starting point in data space during the search process. We demonstrate the power of the BLRC with more lifelike and complex reaching motion.

Moving-Target Pursuit Algorithm Using Improved Tracking Strategy

Pursuing a moving target in modern computer games presents several challenges to situated agents, including real-time response, large-scale search space, severely limited computation resources, incomplete environmental knowledge, adversarial escaping strategy, and outsmarting the opponent. In this paper, we propose a novel tracking automatic optimization moving-target pursuit (TAO-MTP) algorithm employing improved tracking strategy to effectively address all challenges above for the problem involving single hunter and single prey. TAO-MTP uses a queue to store preys trajectory, and simultaneously runs real-time adaptive A* (RTAA*) repeatedly to approach the optimal position updated periodically in the trajectory within limited steps, which makes the overall pursuit cost smallest. In the process, the hunter speculatively moves to any position explored in the trajectory, not necessarily the optimal position, to speed up convergence, and then directly moves along the trajectory to pursue the prey. Moreover, automatic optimization methods, such as reducing trajectory storage and optimizing pursuit path, are used to further enhance its performance. As long as the hunters moving speed is faster than that of the prey, and its sense scope is large enough, it will eventually capture the prey. Experiments using commercial game maps show that TAO-MTP is independent of adversarial escaping strategy, and outperforms all the classic and state-of-the-art moving-target pursuit algorithms such as extended moving-target search (eMTS), path refinement moving-target search (PR MTS), moving-target adaptive A* (MTAA*), and generalized adaptive A* (GAA*).

The framework and implementation of Virtual Network Marathon

In this paper, we present an exergame called VNM (Virtual Network Marathon). The VNM employs devised treadmills for immersive virtual running in local network or on Internet, which are embedded with various sensors for collecting the body performance data and connected with computers or Set-Top Box (STB) to synchronously control the players avatar in the virtual game environment. VNM is implemented on a novel ISCAL model (Immersion, Scientificalness, Competitiveness, Adaptability and Learning) model for exergames design, The exercise time and intensity strictly conform to the physical exercise guidelines of the ACSM (American College of Sports Medicine), and a novel demonstration-based non-player modeling technique is employed to simulate the marathon race crowd. The VNM also allows players to learn the Chinese culture and the Olympic knowledge in the tour mode during or after the running exercise. The user study of VNM indicates that the proposed ISCAL model can be successfully applied in the exergame design.

Learning-Based Shadow Recognition and Removal From Monochromatic Natural Images

This paper addresses the problem of recognizing and removing shadows from monochromatic natural images from a learning-based perspective. Without chromatic information, shadow recognition and removal are extremely challenging in this paper, mainly due to the missing of invariant color cues. Natural scenes make this problem even harder due to the complex illumination condition and ambiguity from many near-black objects. In this paper, a learning-based shadow recognition and removal scheme is proposed to tackle the challenges above-mentioned. First, we propose to use both shadow-variant and invariant cues from illumination, texture, and odd order derivative characteristics to recognize shadows. Such features are used to train a classifier via boosting a decision tree and integrated into a conditional random field, which can enforce local consistency over pixel labels. Second, a Gaussian model is introduced to remove the recognized shadows from monochromatic natural scenes. The proposed scheme is evaluated using both qualitative and quantitative results based on a novel database of hand-labeled shadows, with comparisons to the existing state-of-the-art schemes. We show that the shadowed areas of a monochromatic image can be accurately identified using the proposed scheme, and high-quality shadow-free images can be precisely recovered after shadow removal.

An efficient method of crowd aggregation computation in public areas

The crowd stampede and terrorist attacks in public areas have now become more serious and dangerous threats due to the rapid increase in the population and scale of cities. Therefore, the analysis of crowd aggregation behavior has been a new research focus in the field of intelligent video surveillance. However, such public area scenes not only contain moving crowd but also contain other types of objects. The sizes of these objects are usually small, which make their appearances quite similar. Moreover, the individuals in a crowd move randomly and often occlude each other. All the above factors make the analysis of crowd aggregation very difficult. In this paper, the authors attempt to solve this problem in three aspects. First, a novel global feature is used to represent the moving crowd. This feature can well describe the spatial and the temporal motion information of points-of-interest. Second, a strategy is adopted to cluster the feature points first and then calculate the collectiveness. This makes the collectiveness computation of individual groups more consistent and effective. Finally, more comprehensive collective crowd descriptors are proposed to provide a detailed description of the crowd status. Based on the proposed descriptor, the authors realize the evolution analysis of the group movement and the crowd abnormal detection. The experiment results show that the proposed method is able to efficiently compute the crowd collectiveness in various public areas and provide a reliable reference for the public safety management.

L4RW: Laziness‐based Realistic Real‐time Responsive Rebalance in Walking

We present a novel L4RW (Laziness‐based Realistic Real‐time Responsive Rebalance in Walking) technique to synthesize 4RW animations under unexpected external perturbations with minimal locomotion effort. We first devise a lazy dynamic rebalance model, which specifies the dynamic balance conditions, defines the rebalance effort, and selects the suitable rebalance strategy automatically using the laziness law after an unexpected perturbation. Based on the model, L4RW searches over a motion capture (mocap) database for an appropriate motion segment to follow, and the transition‐to motions is generated by interpolating the active response dynamic motion. A support vector machine (SVM) based training, classification, and predication algorithm is applied to reduce the search space, and it is trained offline only once. Our algorithm classifies the mocap database into many rebalance strategy‐specified subsets and then online predicts responsive motions in the subset according to the selected strategy. The rebalance effort, the ‘extrapolated center of mass’ (XCoM) and environment constraints are selected as feature attributes for the SVM feature vector. Furthermore, the subsets segments are sorted through the rebalance effort, then our algorithm searches for an acceptable segment starting from the least‐effort segment. Compared with previous methods, our search increases speed by over two orders of magnitude, and our algorithm creates more realistic and smooth 4RW animation.

A New Remote Health-Care System Based on Moving Robot Intended for the Elderly at Home

Nowadays, due to the growing need for remote care and the constantly increasing popularity of mobile devices, a large amount of mobile applications for remote care support has been developed. Although mobile phones are very suitable for young people, there are still many problems related to remote health care of the elderly. Due to hearing loss or limited movements, it is difficult for the elderly to contact their families or doctors via real-time video call. In this paper, we introduce a new remote health-care system based on moving robots intended for the elderly at home. Since the proposed system is an online system, the elderly can contact their families and doctors quickly anytime and anywhere. Besides call, our system involves the accurate indoor object detection algorithms and automatic health data collection, which are not included in existing remote care systems. Therefore, the proposed system solves some challenging problems related to the elderly care. The experiment has shown that the proposed care system achieves excellent performance and provides good user experience.