Pedestrian Tracking Combined with Deep Learning and Camera Network Topology in Non-overlapping Multi-camera Surveillance

2019 ◽  
Author(s):  
Mianjin Wei ◽  
Jihong Pei
Keyword(s):  
Deep Learning ◽  
Network Topology ◽  
Camera Network ◽  
Pedestrian Tracking ◽  
Camera Surveillance

A unified framework for automated person re-indentification

2020 ◽  
Vol 71 (7) ◽  
pp. 868-880
Author(s):  
Nguyen Hong-Quan ◽  
Nguyen Thuy-Binh ◽  
Tran Duc-Long ◽  
Le Thi-Lan
Keyword(s):  
Deep Learning ◽  
Video Analysis ◽  
Camera Network ◽  
Unified Framework ◽  
Person Detection ◽  
Practical Applications ◽  
Detection And Tracking ◽  
Analysis System ◽  
Bounding Boxes

Along with the strong development of camera networks, a video analysis system has been become more and more popular and has been applied in various practical applications. In this paper, we focus on person re-identification (person ReID) task that is a crucial step of video analysis systems. The purpose of person ReID is to associate multiple images of a given person when moving in a non-overlapping camera network. Many efforts have been made to person ReID. However, most of studies on person ReID only deal with well-alignment bounding boxes which are detected manually and considered as the perfect inputs for person ReID. In fact, when building a fully automated person ReID system the quality of the two previous steps that are person detection and tracking may have a strong effect on the person ReID performance. The contribution of this paper are two-folds. First, a unified framework for person ReID based on deep learning models is proposed. In this framework, the coupling of a deep neural network for person detection and a deep-learning-based tracking method is used. Besides, features extracted from an improved ResNet architecture are proposed for person representation to achieve a higher ReID accuracy. Second, our self-built dataset is introduced and employed for evaluation of all three steps in the fully automated person ReID framework.

scholarly journals A Strong and Efficient Baseline for Vehicle Re-Identification Using Deep Triplet Embedding

2020 ◽  
Vol 10 (1) ◽  
pp. 27-45 ◽  
Author(s):  
Ratnesh Kumar ◽  
Edwin Weill ◽  
Farzin Aghdasi ◽  
Parthasarathy Sriram
Keyword(s):  
Deep Learning ◽  
Smart City ◽  
State Of The Art ◽  
Loss Functions ◽  
Camera Network ◽  
Formal Evaluation ◽  
Cross Domain ◽  
Extensive Evaluation ◽  
Data Points ◽  
Triplet Loss

AbstractIn this paper we tackle the problem of vehicle re-identification in a camera network utilizing triplet embeddings. Re-identification is the problem of matching appearances of objects across different cameras. With the proliferation of surveillance cameras enabling smart and safer cities, there is an ever-increasing need to re-identify vehicles across cameras. Typical challenges arising in smart city scenarios include variations of viewpoints, illumination and self occlusions. Most successful approaches for re-identification involve (deep) learning an embedding space such that the vehicles of same identities are projected closer to one another, compared to the vehicles representing different identities. Popular loss functions for learning an embedding (space) include contrastive or triplet loss. In this paper we provide an extensive evaluation of triplet loss applied to vehicle re-identification and demonstrate that using the recently proposed sampling approaches for mining informative data points outperform most of the existing state-of-the-art approaches for vehicle re-identification. Compared to most existing state-of-the-art approaches, our approach is simpler and more straightforward for training utilizing only identity-level annotations, along with one of the smallest published embedding dimensions for efficient inference. Furthermore in this work we introduce a formal evaluation of a triplet sampling variant (batch sample) into the re-identification literature. In addition to the conference version [24], this submission adds extensive experiments on new released datasets, cross domain evaluations and ablation studies.

Pedestrian Tracking Across Panning Camera Network

2010 ◽  
Vol 8 (1) ◽  
pp. 10-25 ◽  
Author(s):  
Yasuhide Hyodo ◽  
Kaichi Fujimura ◽  
Takeshi Naito ◽  
Shunsuke Kamijo
Keyword(s):  
Camera Network ◽  
Pedestrian Tracking

scholarly journals Data Processing Using Artificial Neural Networks

2020 ◽  
Author(s):  
Wesam Salah Alaloul ◽  
Abdul Hannan Qureshi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Topology ◽  
Basic Concept ◽  
Ann Model ◽  
Learning Models ◽  
Learning Rules ◽  
Invariance Problem ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The artificial neural network (ANN) is a machine learning (ML) methodology that evolved and developed from the scheme of imitating the human brain. Artificial intelligence (AI) pyramid illustrates the evolution of ML approach to ANN and leading to deep learning (DL). Nowadays, researchers are very much attracted to DL processes due to its ability to overcome the selectivity-invariance problem. In this chapter, ANN has been explained by discussing the network topology and development parameters (number of nodes, number of hidden layers, learning rules and activated function). The basic concept of node and neutron has been explained, with the help of diagrams, leading to the ANN model and its operation. All the topics have been discussed in such a scheme to give the reader the basic concept and clarity in a sequential way from ANN perceptron model to deep learning models and underlying types.

scholarly journals Dynamic Object Path Detection in a Network of Surveillance Cameras

2013 ◽  
Vol 1 (1) ◽  
pp. 11-19
Author(s):  
H.H. Weerasena ◽  
P. B. S. Bandara ◽  
J.R.B. Kulasekara ◽  
B. M. B. Dassanayake ◽  
U. A. A. Niroshika ◽  
...  
Keyword(s):  
Moving Objects ◽  
Behavioral Changes ◽  
Surveillance Systems ◽  
Camera Network ◽  
Important Goal ◽  
Public And Private ◽  
3D Space ◽  
Detection And Tracking ◽  
Stereo Cameras ◽  
Camera Surveillance

Today, automated camera surveillance systems play a major role in securing public and private premises to ensure security and to reduce crime by detecting behavioral changes of moving objects. The important goal of such a surveillance system is to reduce human intervention while at the same time, provide accurate detection of moving objects. Many researchers have attempted to automate different aspects of camera surveillance such as tracking humans, traffic controlling, ground surveillance, etc. However, a system that overcomes overall difficulties that arise in the task of object detection and object tracking has not been developed because of high variance in the problem domain. The proposed system tracks the path of a locked object through a network of cameras. In contrast to traditional methods where the operators have to switch the screens manually to find the target objects, the proposed technique, once locked to an object; automatically tracks it through a camera network and generates the path on a map. We propose to use stereo cameras to enhance the detection and tracking of objects in 3D space.

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