scholarly journals Joint Detection and Identification Feature Learning for Person Search

2017 ◽  
Author(s):  
Tong Xiao ◽  
Shuang Li ◽  
Bochao Wang ◽  
Liang Lin ◽  
Xiaogang Wang
Keyword(s):  
Feature Learning ◽  
Joint Detection ◽  
Detection And Identification ◽  
Person Search

Multi-object Tracking by Joint Detection and Identification Learning

2019 ◽  
Vol 50 (1) ◽  
pp. 283-296
Author(s):  
Bo Ke ◽  
Huicheng Zheng ◽  
Lvran Chen ◽  
Zhiwei Yan ◽  
Ye Li
Keyword(s):  
Object Tracking ◽  
Joint Detection ◽  
Detection And Identification

scholarly journals Person Search via Deep Integrated Networks

2019 ◽  
Vol 10 (1) ◽  
pp. 188
Author(s):  
Ju-Chin Chen ◽  
Cheng-Feng Wu ◽  
Chun-Huei Chen ◽  
Cheng-Rong Lin
Keyword(s):  
Accuracy Rate ◽  
Integrated Networks ◽  
Practical Applications ◽  
Detection And Identification ◽  
Person Search ◽  
Background Clutter ◽  
Manual Processing ◽  
Active Research ◽  
Bounding Boxes ◽  
Problem Setting

This study proposes an integrated deep network consisting of a detection and identification module for person search. Person search is a very challenging problem because of the large appearance variation caused by occlusion, background clutter, pose variations, etc., and it is still an active research issue in the academic and industrial fields. Although various studies have been proposed, following the protocols of the person re-identification (ReID) benchmarks, most existing works take cropped pedestrian images either from manual labelling or a perfect detection assumption. However, for person search, manual processing is unavailable in practical applications, thereby causing a gap between the ReID problem setting and practical applications. One fact is also ignored: an imperfect auto-detected bounding box or misalignment is inevitable. We design herein a framework for the practical surveillance scenarios in which the scene images are captured. For person search, detection is a necessary step before ReID, and previous studies have shown that the precision of detection results has an influence on person ReID. The detection module based on the Faster R-CNN is used to detect persons in a scene image. For identifying and extracting discriminative features, a multi-class CNN network is trained with the auto-detected bounding boxes from the detection module, instead of the manually cropped data. The distance metric is then learned from the discriminative features output by the identification module. According to the experimental results of the test performed in the scene images, the multi-class CNN network for the identification module can provide a 62.7% accuracy rate, which is higher than that for the two-class CNN network.

scholarly journals Hierarchical Online Instance Matching for Person Search

2020 ◽  
Vol 34 (07) ◽  
pp. 10518-10525
Author(s):  
Di Chen ◽  
Shanshan Zhang ◽  
Wanli Ouyang ◽  
Jian Yang ◽  
Bernt Schiele
Keyword(s):  
State Of The Art ◽  
Pedestrian Detection ◽  
Feature Learning ◽  
Optimal Solution ◽  
Unified Framework ◽  
Discrimination Power ◽  
Hierarchical Relationship ◽  
Instance Matching ◽  
Image Dataset ◽  
Person Search

Person Search is a challenging task which requires to retrieve a person's image and the corresponding position from an image dataset. It consists of two sub-tasks: pedestrian detection and person re-identification (re-ID). One of the key challenges is to properly combine the two sub-tasks into a unified framework. Existing works usually adopt a straightforward strategy by concatenating a detector and a re-ID model directly, either into an integrated model or into separated models. We argue that simply concatenating detection and re-ID is a sub-optimal solution, and we propose a Hierarchical Online Instance Matching (HOIM) loss which exploits the hierarchical relationship between detection and re-ID to guide the learning of our network. Our novel HOIM loss function harmonizes the objectives of the two sub-tasks and encourages better feature learning. In addition, we improve the loss update policy by introducing Selective Memory Refreshment (SMR) for unlabeled persons, which takes advantage of the potential discrimination power of unlabeled data. From the experiments on two standard person search benchmarks, i.e. CUHK-SYSU and PRW, we achieve state-of-the-art performance, which justifies the effectiveness of our proposed HOIM loss on learning robust features.

FLAG: feature learning with additional guidance for person search

2020 ◽  
Author(s):  
Zhicheng Chen ◽  
Xinbi Lv ◽  
Tianli Sun ◽  
Cairong Zhao ◽  
Wei Chen
Keyword(s):  
Feature Learning ◽  
Person Search

Identification of hepatitis a virus in cell cultures by solid phase immune electron microscopy

1986 ◽  
Vol 44 ◽  
pp. 238-239
Author(s):  
C.D. Humphrey ◽  
T.L. Cromeans ◽  
E.H. Cook ◽  
D.W. Bradley
Keyword(s):  
Electron Microscopy ◽  
Liquid Phase ◽  
Cell Cultures ◽  
Rapid Detection ◽  
Hepatitis A ◽  
Hepatitis A Virus ◽  
Solid Phase ◽  
Immune Electron Microscopy ◽  
Detection And Identification

There is a variety of methods available for the rapid detection and identification of viruses by electron microscopy as described in several reviews. The predominant techniques are classified as direct electron microscopy (DEM), immune electron microscopy (IEM), liquid phase immune electron microscopy (LPIEM) and solid phase immune electron microscopy (SPIEM). Each technique has inherent strengths and weaknesses. However, in recent years, the most progress for identifying viruses has been realized by the utilization of SPIEM.

115: Detection and Identification of Bacterial DNA and HIV Gag in the Semen of HIV Infected Men

2004 ◽  
Vol 171 (4S) ◽  
pp. 30-30
Author(s):  
Robert C. Eyre ◽  
Ann A. Kiessling ◽  
Thomas E. Mullen ◽  
Rachel L. Kiessling
Keyword(s):  
Bacterial Dna ◽  
Detection And Identification
Sign in / Sign up

Export Citation Format

Share Document