Dr. Weihai Li Profile

Dr. Weihai Li

at Univ of Science and Technology of China

SPIE Involvement:

Author

Publications (4)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 12 June 2020 Paper

Target analysis based anomaly detection in surveillance videos

Jie Zhang, Weihai Li

Proceedings Volume 11519, 1151908 (2020) https://doi.org/10.1117/12.2573117

KEYWORDS: Video surveillance

Read Abstract +

Abnormal behavior detection in surveillance video is a pivotal part of the intelligent city. Most of the existing methods only consider how to detect anomalies, with less considering to explain the reason of the anomalies. In this work, we investigate an orthogonal perspective based on the reason of these abnormal behaviors. We propose a multivariate fusion method that analyzes each target through three branches: object, action and motion. The object branch focuses on the appearance information, the motion branch focuses on the distribution of the motion features, and the action branch focuses on the action category of the target. The information that these branches focus on is different, and they can complement each other and jointly detect abnormal behavior. The final abnormal score can then be obtained by combining the abnormal scores of the three branches. In the action branch, we also propose an action recognition module using inter-frame information to solve the multi-target and multi-action recognition in the surveillance video, which is not utilized before in the anomaly detection field. The proposed method outperforms the state-of-the-art methods and also can explain why the target is detected as an anomaly.

Proceedings Article | 9 August 2018 Paper

Enhanced light field depth estimation for complex occlusion scenes

Ying Jia, Weihai Li

Proceedings Volume 10806, 1080669 (2018) https://doi.org/10.1117/12.2503005

KEYWORDS: Optical flow, Cameras, Machine vision, Computer vision technology, Data modeling, Feature extraction, Imaging systems

Read Abstract +

Depth estimation is always a hot topic in computer vision, which shows new vitality with the rise of light field camera. Nevertheless, occlusion is a tough problem, which degrades the precision of the acquired depth map. Although previous works have proposed some effective methods to solve this problem, regrettably they are deficient. In this paper, we extend previous single occlusion model into complex occlusion condition, adopt optical flow algorithm to get candidate occlusion points, combine multiple features to separate the angular patch, and employ more reasonable data cost to get the depth map. Because the proposed algorithm is more suitable for light field data, experimental results show that the proposed algorithm has a better performance than state-of-the-art algorithms on synthetic datasets and real world images captured by light field camera, especially for complex occlusion scenes.

Proceedings Article | 21 July 2017 Paper

Multi-object tracking based on two-layer occlusion handling

Huiling Wu, Weihai Li

Proceedings Volume 10420, 104200K (2017) https://doi.org/10.1117/12.2281574

KEYWORDS: Convolution, Electronic filtering, Particle swarm optimization

Read Abstract +

Multi-object tracking is particularly challenging in many scenarios with similar appearance and frequent occlusions among targets. In this paper, we present an online detection-based multi-object tracking method. In each frame, kernerlized convolution filter are adopted to track isolated and un-occluded targets. To overcoming fixed scale in KCF, trackers are associated with detection responses. If a target is associated with a detection, then the target size is updated by the average of this detection size and the previous estimated size. When occlusions are detected, the multiple interaction among targets is formulated as an optimization problem and we explore two-layer hierarchical Particle Swarm Optimization algorithm for the optimal solution. The first layer is designed for the superficial targets which is visible. The second layer is designed for the bottom occluded targets which can guided by first visible layer and we propose to incorporate the attractive force into the particle evolution process. Experimental results on public datasets demonstrate that our proposed method alleviating drifting problem and effectively reduces ID switches and lost trajectories.

Proceedings Article | 21 July 2017 Paper

Multi-object tracking based on formation stability

Liang Xu, Weihai Li

Proceedings Volume 10420, 104200L (2017) https://doi.org/10.1117/12.2281982

Read Abstract +

Multi-object tracking in semi-crowded environment is a difficult task. Traditional methods relying only on visual cues, like appearance and simple motion prediction fails because of frequent occlusions. In this paper, a new method based on formation stability is proposed to learn the interaction information among pedestrians. whatever the relationship of a pedestrian with others is, it will remain unchanged for a while. That is, when a pedestrian is occluded, the relationship with others will stay the same after the reappearance of the pedestrian. So trajectory clips caused by occlusions can be associated together. Effectiveness of this method is validated by experiments.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years