Yie-Tarng Chen

Video anomaly detection and localization using hierarchical feature representation and Gaussian process regression

This paper presents a hierarchical framework for detecting local and global anomalies via hierarchical feature representation and Gaussian process regression. While local anomaly is typically detected as a 3D pattern matching problem, we are more interested in global anomaly that involves multiple normal events interacting in an unusual manner such as car accident. To simultaneously detect local and global anomalies, we formulate the extraction of normal interactions from training video as the problem of efficiently finding the frequent geometric relations of the nearby sparse spatio-temporal interest points. A codebook of interaction templates is then constructed and modeled using Gaussian process regression. A novel inference method for computing the likelihood of an observed interaction is also proposed. As such, our model is robust to slight topological deformations and can handle the noise and data unbalance problems in the training data. Simulations show that our system outperforms the main state-of-the-art methods on this topic and achieves at least 80% detection rates based on three challenging datasets.

A hybrid human fall detection scheme

This paper presents a novel video-based human fall detection system that can detect a human fall in real-time with a high detection rate. This fall detection system is based on an ingenious combination of skeleton feature and human shape variation, which can efficiently distinguish “fall-down” activities from “fall-like” ones. The experimental results indicate that the proposed human fall detection system can achieve a high detection rate and low false alarm rate.

Gaussian Process Regression-Based Video Anomaly Detection and Localization With Hierarchical Feature Representation

This paper presents a hierarchical framework for detecting local and global anomalies via hierarchical feature representation and Gaussian process regression (GPR) which is fully non-parametric and robust to the noisy training data, and supports sparse features. While most research on anomaly detection has focused more on detecting local anomalies, we are more interested in global anomalies that involve multiple normal events interacting in an unusual manner, such as car accidents. To simultaneously detect local and global anomalies, we cast the extraction of normal interactions from the training videos as a problem of finding the frequent geometric relations of the nearby sparse spatio-temporal interest points (STIPs). A codebook of interaction templates is then constructed and modeled using the GPR, based on which a novel inference method for computing the likelihood of an observed interaction is also developed. Thereafter, these local likelihood scores are integrated into globally consistent anomaly masks, from which anomalies can be succinctly identified. To the best of our knowledge, it is the first time GPR is employed to model the relationship of the nearby STIPs for anomaly detection. Simulations based on four widespread datasets show that the new method outperforms the main state-of-the-art methods with lower computational burden.

A flexible service model for advance reservation

Abstract Although advance reservations become more important in many applications, optimization of system resources usage is still an untouched research issue. The greatly fluctuating workload between day and night poses a problem for the advance reservation system design. In this paper, a flexible reservation model and a request scheduling scheme are proposed to tackle this issue. The flexible reservation model introduces a flexible interval for starting time for advance requests to support request scheduling. Therefore, the flexible intervals of advance requests can be represented as a multistage digraph and then the optimal scheduling can be found on the shortest path of the digraph. Simulation results confirm significant improvement in terms of the acceptance ratio for advance requests.

Abnormal crowd behavior detection and localization using maximum sub-sequence search

This paper presents a novel framework for anomaly event detection and localization in crowded scenes. We propose an anomaly detector that extends the Bayes classifier from multi-class to one-class classification to characterize normal events. We also propose a localization scheme for anomaly localization as a maximum subsequence problem in a video sequence. The maximum subsequence algorithm locates an anomaly event by discovering the optimal collection of successive patches with spatial proximity over time without prior knowledge of the size, start and end of the anomaly event. Our localization scheme can locate multiple occurrences of abnormal events in spite of noise. Experimental results on the well-established UCSD dataset show that the proposed framework significantly outperforms state-of-the-art methods up to 53.55% localization rate. This study concludes that the localization framework plays an important role in abnormal event detection.

A video‐based human fall detection system for smart homes

Abstract In recent years, the global population has begun to age rapidly. Automatic fall detection for senior citizens has become an important issue for smart homes. This paper presents a novel video‐based human fall detection system that can detect a human fall in real‐time with a high detection rate. This fall detection system is based on an ingenious combination of skeleton features and human shape variations, which can efficiently distinguish “fall‐down incidents” from “fall‐like” ones. The experimental results indicate that the proposed human fall detection system can achieve a high detection rate and low false alarm rate.

Joint Carrier Frequency Offset and Direction of Arrival Estimation via Hierarchical ESPRIT for Interleaved OFDMA/SDMA Uplink Systems

In this paper, we propose an efficient algorithm to jointly estimate the directions of arrival (DOAs) and carrier frequency offsets (CFOs) in interleaved orthogonal frequency division multiple access / space division multiple access (OFDMA/SDMA) uplink networks. The algorithm makes use of the signal structure by estimating the CFOs and DOAs in a hierarchical tree structure, in which two CFO estimations and one DOA estimation are employed alternatively. One special feature in the proposed algorithm is that the algorithm proceeds in a coarse-fine manner with temporal filtering or spatial beamforming being invoked between the parameter estimations to decompose the signals progressively into subgroups so as to enhance the estimation accuracy and lower the computational overhead. Simulations show that the proposed algorithm can provide satisfactory performance with increased channel capacity.

An efficient subsequence search for video anomaly detection and localization

This paper presents a novel framework for anomaly event detection and localization in crowded scenes. For anomaly detection, one-class support vector machine with Bayesian derivation is applied to detect unusual events. We also propose a novel event representation, called subsequence, which refers to a time series of spatial windows in proximity. Unlike recent works encoded an event with a 3D bounding box which may contain irrelevant information, e.g. background, a subsequence can concisely capture the unstructured property of an event. To efficiently locate anomalous subsequences in a video space, we propose the maximum subsequence search. The proposed search algorithm integrates local anomaly scores into a global consistent detection so that the start and end of an abnormal event can be determined under false and missing detections. Experimental results on two public datasets show that our method is robust to the illumination change and achieve at least 80% localization rate which approximately doubles the accuracy of recent works. This study concludes that anomaly localization is crucial in finding abnormal events.

Iterative Multiuser Detection with Soft Interference Cancellation for Multirate MC-CDMA Systems

This paper presents an effective multi-rate multiuser detector (MUD) for the uplink of single-input multiple- output (SIMO) multi-carrier code division multiple access (MC- CDMA) systems. The MUD considered is an iterative receiver which utilizes the soft information to refine the estimation of the interference to enhance the interference cancellation capability. More specifically, users with different transmission rates are classified into separate groups and, in each iteration, these groups of users are detected sequentially based on a set of minimum mean-squared error (MMSE) group detectors with the removal of multiple access interferences (MAI) group by group. Furthermore, the estimated interferences in each group, either from the same or the other groups, are refined successively with the assistance of the soft information in the symbol detection process. Conducted simulations show that the proposed MUD, with moderate computational overhead, can effectively suppress the MAI to render superior performance compared with previous works.

Genetic algorithm-assisted joint quantised precoding and transmit antenna selection in multi-user multi-input multi-output systems

This study presents a simple and efficient genetic algorithm-assisted approach for joint quantised precoding and transmit antenna selection based on the criterion of maximum capacity. The objective is to alleviate the effect of multi-user interference and to reduce hardware costs, such as the cost of radio frequency chains associated with antennas in the downlink of multi-input multi-output systems with limited feedback. To avoid the enormous search effort required by existing approaches, the authors propose a novel variant of the conventional genetic algorithm, called the hybrid genetic algorithm, in which each chromosome is divided into a bit string for precoding vector selection and an integer string for transmit antenna selection. In addition, new crossover and mutation operations are employed to accommodate these new chromosomes. The results of simulations show that the performance of the proposed approach is close to that of the exhaustive search method, but its computational complexity is substantially lower.