scispace - formally typeset
Search or ask a question
Author

Zhaokai Wang

Bio: Zhaokai Wang is an academic researcher from Beihang University. The author has contributed to research in topics: Outlier & Local outlier factor. The author has an hindex of 3, co-authored 6 publications receiving 28 citations.

Papers
More filters
Journal ArticleDOI
Shubin Su1, Limin Xiao1, Li Ruan1, Fei Gu1, Shupan Li1, Zhaokai Wang1, Rongbin Xu1 
TL;DR: This paper redefines a local outlier factor called local deviation coefficient (LDC) by taking full advantage of the distribution of the object and its neighbors and proposes a safe non-outlier objects elimination approach named as rough clustering based on multi-level queries (RCMLQ) to preprocess the datasets to eliminate the non- outlier objects to the utmost.
Abstract: After the local outlier factor was first proposed, there is a large family of local outlier detection approaches derived from it. Since the existing approaches only focus on the extent of overall separation between an object and its neighbors, and ignore the degree of dispersion between them, the precision of these approaches will be affected by various degrees in the scattered datasets. In addition, the outlier data occupy a relatively small amount in the dataset, but the existing approaches need to perform local outlier factor calculation on all data during the outlier detection, which greatly reduces the efficiency of the algorithms. In this paper, we redefine a local outlier factor called local deviation coefficient (LDC) by taking full advantage of the distribution of the object and its neighbors. And then, we propose a safe non-outlier objects elimination approach named as rough clustering based on multi-level queries (RCMLQ) to preprocess the datasets to eliminate the non-outlier objects to the utmost. Finally, an efficient local outlier detection approach named as efficient density-based local outlier detection for scattered data (E2DLOS) is proposed based on the LDC and RCMLQ. The RCMLQ greatly reduces the amount of data that needs to be quantified for local outlier factor and the LDC is more sensitive to the degree of anomaly of the scattered datasets, and so the E2DLOS improves the existing local outlier detection approaches in time efficiency and detection accuracy. Experiments show that the LDC can better reflect the true abnormal situations of the data for the scattered datasets. And the RCMLQ can be used in parallel with the traditional methods of improving the efficiency of the nearest neighbor search, which can further improve the efficiency of the E2DLOS algorithm by about 16%.

25 citations

Journal ArticleDOI
TL;DR: This paper proposes an adaptive template matching-based single object tracking algorithm framework to achieve template update online, based on the Faster-RCNN model, and presents a parallel strategy to accelerate the process of template matching.

15 citations

Patent
01 Jun 2018
TL;DR: In this article, an abnormal state detecting method faced to multiple data streams, which can efficiently detect the state change of every data flow at every moment in real time, is presented.
Abstract: The invention provides an abnormal state detecting method faced to multiple data streams, which can efficiently detect the state change of every data flow at every moment in real time. The method includes steps of 1, collecting multiple data stream parameters; 2, initializing a detection algorithm; 3, seeking for a k neighbor domain set of a data object reached at the current moment from a singledata flow gliding window and calculating its local stray coefficient; 4, updating local stray coefficient of the neighbor domain set of the current data object of every data stream; 5, calculating thereference value for judging the abnormal state of the data streams at current moment on the basis of the stray information of single data flow; 6, calculating the reference value for judging the abnormal state of the data streams at current moment on the basis of the multiple data streams stray information of data snapshot; 7, calculating the current stray coefficient of every data; 8, judging the abnormal state of every data flow at current moment; 9, updating the gliding window data set and the data snapshot set of the data flow.

4 citations

Proceedings ArticleDOI
Zhaokai Wang1, Limin Xiao1, Rongbin Xu1, Shubin Su1, Shupan Li1, Yao Song1 
14 Jul 2019
TL;DR: This paper presents a fully convolutional neural network to tackle the mapping between single view RGB images and depth maps, and proposes long skip connections in up-sampling stage to reuse the feature maps which is proved to enhance the result experimentally.
Abstract: This paper presents a fully convolutional neural network to tackle the mapping between single view RGB images and depth maps. To regress the depth maps from monocular images, we leverage deep short skip connections in residual learning for extracting features rather than using hand-crafted features. We further propose long skip connections in up-sampling stage to reuse the feature maps which is proved to enhance the result experimentally. To show the impact of loss functions in monocular depth map predictions, we train our model with kind of loss functions and compare the results qualitatively and quantitatively. The proposed model outperforms all current state-of-the-art results with less training data as well as less than half of training epochs in two standard benchmark data sets without any post-processing procedures or other refinement steps.

4 citations

Patent
15 May 2018
TL;DR: In this paper, a continuous monitored object-oriented exception detection method was proposed, which can detect exception states of continuous monitored objects in real-time by collecting parameters which represent the exception states and generating a sliding window-based continuous monitoring data stream.
Abstract: The invention provides a continuous monitored object-oriented exception detection method, which can detect exception states of continuous monitored objects in real time. The method comprises the following steps of 1, collecting parameters which represent the exception states of the monitored objects, and generating a sliding window-based continuous monitoring data stream; 2, initializing a detection algorithm to generate a sliding window-based data set and an exception value sequence of a monitored object; 3, searching for a k-neighborhood set of a data object reached at a current moment, andcalculating a local outlier coefficient; 4, updating the local outlier coefficient of a k-neighborhood object of the data object reached at the current moment; 5, calculating a reference value of a local outlier coefficient of historical data in judging the exception state of the monitored object at the current moment; 6, calculating an outlier coefficient of the monitored object at the current moment; 7, judging a possible exception state of the monitored object at the current moment; 8, updating the data set of the sliding window of the monitoring data stream and the exception value sequenceof the monitored object; and 9, determining the exception state of the monitored object.

3 citations


Cited by
More filters
Journal Article
TL;DR: In this article, the authors proposed a measure on local outliers based on a symmetric neighborhood relationship, which considers both neighbors and reverse neighbors of an object when estimating its density distribution.
Abstract: Mining outliers in database is to find exceptional objects that deviate from the rest of the data set. Besides classical outlier analysis algorithms, recent studies have focused on mining local outliers, i.e., the outliers that have density distribution significantly different from their neighborhood. The estimation of density distribution at the location of an object has so far been based on the density distribution of its k-nearest neighbors [2,11]. However, when outliers are in the location where the density distributions in the neighborhood are significantly different, for example, in the case of objects from a sparse cluster close to a denser cluster, this may result in wrong estimation. To avoid this problem, here we propose a simple but effective measure on local outliers based on a symmetric neighborhood relationship. The proposed measure considers both neighbors and reverse neighbors of an object when estimating its density distribution. As a result, outliers so discovered are more meaningful. To compute such local outliers efficiently, several mining algorithms are developed that detects top-n outliers based on our definition. A comprehensive performance evaluation and analysis shows that our methods are not only efficient in the computation but also more effective in ranking outliers.

321 citations

Journal ArticleDOI
TL;DR: This survey presents a comprehensive and organized review of the progress of outlier detection methods from 2000 to 2019 and categorizes them into different techniques from diverse outlier Detection techniques, such as distance-, clustering-, density-, ensemble-, and learning-based methods.
Abstract: Detecting outliers is a significant problem that has been studied in various research and application areas. Researchers continue to design robust schemes to provide solutions to detect outliers efficiently. In this survey, we present a comprehensive and organized review of the progress of outlier detection methods from 2000 to 2019. First, we offer the fundamental concepts of outlier detection and then categorize them into different techniques from diverse outlier detection techniques, such as distance-, clustering-, density-, ensemble-, and learning-based methods. In each category, we introduce some state-of-the-art outlier detection methods and further discuss them in detail in terms of their performance. Second, we delineate their pros, cons, and challenges to provide researchers with a concise overview of each technique and recommend solutions and possible research directions. This paper gives current progress of outlier detection techniques and provides a better understanding of the different outlier detection methods. The open research issues and challenges at the end will provide researchers with a clear path for the future of outlier detection methods.

263 citations

Journal ArticleDOI
29 Dec 2020
TL;DR: In this paper, the authors present a literature review of local outlier detection algorithms in static and stream environments, with an emphasis on Local Outlier Factor (LOF), a density-based technique.
Abstract: Outlier detection is a statistical procedure that aims to find suspicious events or items that are different from the normal form of a dataset. It has drawn considerable interest in the field of data mining and machine learning. Outlier detection is important in many applications, including fraud detection in credit card transactions and network intrusion detection. There are two general types of outlier detection: global and local. Global outliers fall outside the normal range for an entire dataset, whereas local outliers may fall within the normal range for the entire dataset, but outside the normal range for the surrounding data points. This paper addresses local outlier detection. The best-known technique for local outlier detection is the Local Outlier Factor (LOF), a density-based technique. There are many LOF algorithms for a static data environment; however, these algorithms cannot be applied directly to data streams, which are an important type of big data. In general, local outlier detection algorithms for data streams are still deficient and better algorithms need to be developed that can effectively analyze the high velocity of data streams to detect local outliers. This paper presents a literature review of local outlier detection algorithms in static and stream environments, with an emphasis on LOF algorithms. It collects and categorizes existing local outlier detection algorithms and analyzes their characteristics. Furthermore, the paper discusses the advantages and limitations of those algorithms and proposes several promising directions for developing improved local outlier detection methods for data streams.

44 citations

Journal ArticleDOI
TL;DR: Considering the relationship between the current frame and the previous frame of a moving object target in a time series, a temporal regularization strategy to improve the BACF tracker (denoted as TRBACF), a typical representative of the aforementioned trackers is proposed.

28 citations

Proceedings ArticleDOI
01 Jan 2020
TL;DR: This work presents the experimentation and the performance comparison between the Jetson Nano and Jetson TX2 development kits, when implementing the Template Matching method, in order to get an evaluation criterion to select one of them in image processing projects.
Abstract: Template Matching is a widely used method for object detection in digital images, it requires great processing power since it is an exhaustive method that compares the intensity levels of a source image pixel-to-pixel with a template image that contains the object to identify. Nowadays there are dedicated embedded systems that provide high processing capabilities, such as the NVIDIA Jetson family. This work presents the experimentation and the performance comparison between the Jetson Nano and Jetson TX2 development kits, when implementing the Template Matching method, in order to get an evaluation criterion to select one of them in image processing projects. It was carried out to six images with different sizes and two variants in terms of the size of the template image. The processing times for the sequential implementation using the CPUs and the parallel implementation with the GPUs were obtained quantitatively. It was observed that the processing times using the parallel versions on average doubled those of the sequential versions and that the Jetson TX2 exceeded the Jetson Nano in execution speeds.

27 citations