Anomaly Detection Based on Local Nearest Neighbor Distance Descriptor in Crowded Scenes
We propose a novel local nearest neighbor distance (LNND) descriptor for anomaly detection in crowded scenes. Comparing with the commonly used low-level feature descriptors in previous works, LNND descriptor has two major advantages. First, LNND descriptor efficiently incorporates spatial and tempor...
Saved in:
Main Authors: | Xing Hu, Shiqiang Hu, Xiaoyu Zhang, Huanlong Zhang, Lingkun Luo |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2014-01-01
|
Series: | The Scientific World Journal |
Online Access: | http://dx.doi.org/10.1155/2014/632575 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Consistency of the $k$-nearest neighbors rule for functional data
by: Younso, Ahmad
Published: (2023-01-01) -
Efisiensi Big Data Menggunakan Improved Nearest Neighbor
by: Aditya Hari Bawono, et al.
Published: (2019-12-01) -
Radar Target Detection with K-Nearest Neighbor Manifold Filter on Riemannian Manifold
by: Dongao Zhou, et al.
Published: (2024-01-01) -
Random k conditional nearest neighbor for high-dimensional data
by: Jiaxuan Lu, et al.
Published: (2025-01-01) -
A Novel Preferential Diffusion Recommendation Algorithm Based on User’s Nearest Neighbors
by: Fuguo Zhang, et al.
Published: (2017-01-01)