scholarly journals A Distributed Tracking Algorithm for Counting People in Video by Head Detection

2020 ◽  
pp. paper26-1-paper26-12
Author(s):  
Denis Kuplyakov ◽  
Yaroslav Geraskin ◽  
Timur Mamedov ◽  
Anton Konushin
Keyword(s):  
Traffic Control ◽  
Predictive Analytics ◽  
Head Tracking ◽  
People Counting ◽  
Counting Error ◽  
Detection Frequency ◽  
Head Detection ◽  
Customer Services ◽  
Distributed Tracking

We consider the problem of people counting in video surveillance. This is one of the most popular tasks in video analysis because this data can be used for predictive analytics and improvement of customer services, traffic control, etc. Our method is based on the object tracking in video with low framerate. We use the algorithm from [1] as a baseline and propose several modifications that improve the quality of people counting. One of the main modifications is to use a head detector instead of a body detector in the tracking pipeline. Head tracking is proved to be more robust and accurate as the heads are less susceptible to occlusions. To find the intersection of a person with a signal line, we either raise the signal lines to the level of the heads or perform a regression of bodies based on the available head detections. Our experimental evaluation has demonstrated that the modified algorithm surpasses the original in both ac- curacy and computational efficiency, showing a lower counting error on a lower detection frequency.

scholarly journals Practical People Counting Algorithm

2021 ◽  
Author(s):  
Timur Mamedov ◽  
Denis Kuplyakov ◽  
Anton Konushin
Keyword(s):  
Object Tracking ◽  
Traffic Control ◽  
Predictive Analytics ◽  
Head Tracking ◽  
People Counting ◽  
Processor Core ◽  
Speed Up ◽  
Customer Services ◽  
Baseline Algorithm ◽  
Single Processor

In this paper, we consider the problem of people counting in video surveillance. This is an important task in video analysis, because this data can be used for predictive analytics and improvement of customer services, traffic control, etc. The proposed methods are based on object tracking and are able to work on sparse frames, which allows them to work faster and requires minimum computing resources. We use the algorithm from [1] as a baseline, which based on object tracking by head detections. Head tracking in baseline is proved to be more robust and accurate as the heads are less susceptible to occlusions. But this approach has two disadvantages: the height of people is different, which means that people’s heads are in different planes, so the raised signal line doesn’t look so clear, and also because of this, the accuracy of people counting may decrease. In baseline, this problems were solved using head-to-body linear regression, which had to be retrained for each scene, but this complicates the use of the algorithm for practical purposes. In this paper, we propose a new neural network head-to-body regressor, which allows us to solve the mentioned problems at once. Also in this paper, we use a new visual tracking algorithm that allowed us to speed up our solution. In this work, we introduce two methods — distributed modified baseline algorithm with high people counting accuracy and a solution that can run on a single processor core. Our experimental evaluation showed that the proposed modifications are consistent.

ANALISIS DE CALIDAD DE SERVICIO EN REDES IPv6

2016 ◽  
Vol 26 (1) ◽  
pp. 7
Author(s):  
Jose Carlos Tavara Carbajal
Keyword(s):  
Quality Of Service ◽  
Packet Loss ◽  
Traffic Control ◽  
Video Traffic ◽  
Control Software ◽  
Delay Jitter ◽  
End To End Delay ◽  
Analysis Of Results ◽  
Ipv6 Protocol

RESUMENEste documento tiene como objetivo analizar el comportamiento de la calidad del servicio del protocolo IPv6 sobre el tráfico de video, para esto se realizó sobre un entorno real y se llevó acabo el análisis de resultados a través de un software estadístico de control del tráfico.Palabras Clave.-  Calidad de Servicio, Ancho de Banda, Retardo, Fluctuación de Retardo, Pérdidas de Paquetes.ABSTRACTThis paper has aimed to analyze of the service quality of the IPv6 protocol on video traffic, this was about a real environment and was conducted analysis of results through statistical traffic control software. Key words- Quality of Service, Bandwidth, End to end delay, Jitter, Packet loss.

ANALISIS DE CALIDAD DE SERVICIO EN REDES IPv6

2016 ◽  
Vol 26 (1) ◽  
pp. 1
Author(s):  
Jose Carlos Tavara Carbajal
Keyword(s):  
Quality Of Service ◽  
Packet Loss ◽  
Traffic Control ◽  
Video Traffic ◽  
Control Software ◽  
Delay Jitter ◽  
End To End Delay ◽  
Analysis Of Results ◽  
Ipv6 Protocol

Este documento tiene como objetivo analizar el comportamiento de la calidad del servicio del protocolo IPv6 sobre el tráfico de video, para esto se realizó sobre un entorno real y se llevó acabo el análisis de resultados a través de un software estadístico de control del tráfico.Palabras Clave.-  Calidad de Servicio, Ancho de Banda, Retardo, Fluctuación de Retardo, Pérdidas de Paquetes.ABSTRACT  This paper has aimed to analyze of the service quality of the IPv6 protocol on video traffic, this was about a real environment and was conducted analysis of results through statistical traffic control software.  Key words.- Quality of Service, Bandwidth, End to end delay, Jitter, Packet loss.

ANALISIS DE CALIDAD DE SERVICIO EN REDES IPv6

2016 ◽  
Vol 26 (1) ◽  
pp. 7
Author(s):  
Jose Carlos Tavara Carbajal
Keyword(s):  
Quality Of Service ◽  
Packet Loss ◽  
Traffic Control ◽  
Video Traffic ◽  
Control Software ◽  
Delay Jitter ◽  
End To End Delay ◽  
Analysis Of Results ◽  
Ipv6 Protocol

RESUMENEste documento tiene como objetivo analizar el comportamiento de la calidad del servicio del protocolo IPv6 sobre el tráfico de video, para esto se realizó sobre un entorno real y se llevó acabo el análisis de resultados a través de un software estadístico de control del tráfico.Palabras Clave.-  Calidad de Servicio, Ancho de Banda, Retardo, Fluctuación de Retardo, Pérdidas de Paquetes.ABSTRACTThis paper has aimed to analyze of the service quality of the IPv6 protocol on video traffic, this was about a real environment and was conducted analysis of results through statistical traffic control software. Keywords- Quality of Service, Bandwidth, End to end delay, Jitter, Packet loss.

Predicting Customer Churn at QWE Inc.

2017 ◽  
pp. 1-4
Author(s):  
Anton S. Ovchinnikov
Keyword(s):  
Logistic Regression ◽  
Predictive Analytics ◽  
Customer Behavior ◽  
Discrete Events ◽  
Customer Churn ◽  
Start Up ◽  
Customer Services

This case exposes students to predictive analytics as applied to discrete events with logistic regression. The VP of customer services for a successful start-up wants to proactively identify customers most likely to cancel services or “churn.” He assigns the task to one of his associates and provides him with data on customer behavior and his intuition about what drives churn. The associate must generate a list of the customers most likely to churn and the top three reasons for that likelihood.

scholarly journals Algorithm for Counting Cars in Large-scale Video Surveillance Systems

2020 ◽  
pp. 100-108
Author(s):  
Arsenii Shirokov ◽  
Denis Kuplyakov ◽  
Anton Konushin
Keyword(s):  
Video Surveillance ◽  
Large Scale ◽  
Surveillance Systems ◽  
Motion Model ◽  
People Tracking ◽  
Detection Frequency ◽  
Computational Resources ◽  
Sparse Set ◽  
The Given ◽  
Distributed Tracking

The article deals with the problem of counting cars in large-scale video surveillance systems. The proposed method is based on car tracking and counting the number of tracks intersecting the given signal line. We use a distributed tracking algorithm. It reduces the amount of necessary computational resources and increases performance up to realtime by detecting vehicles in a sparse set of frames. We adapted and modified the approach previously proposed for people tracking. Proposed improvement of the speed estimation module and refinement of the motion model reduced the detection frequency by 3 times. The experimental evaluation shows that the proposed algorithm allows reaching an acceptable counting quality with a detection frequency of 3 Hz.

scholarly journals A Soft-YoloV4 for High-Performance Head Detection and Counting

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3096
Author(s):  
Zhen Zhang ◽  
Shihao Xia ◽  
Yuxing Cai ◽  
Cuimei Yang ◽  
Shaoning Zeng
Keyword(s):  
High Performance ◽  
Detection Rate ◽  
Detection Method ◽  
State Of The Art ◽  
Counting Method ◽  
People Counting ◽  
Detection Model ◽  
Detection And Counting ◽  
Head Detection ◽  
Ap Value

Blockage of pedestrians will cause inaccurate people counting, and people’s heads are easily blocked by each other in crowded occasions. To reduce missed detections as much as possible and improve the capability of the detection model, this paper proposes a new people counting method, named Soft-YoloV4, by attenuating the score of adjacent detection frames to prevent the occurrence of missed detection. The proposed Soft-YoloV4 improves the accuracy of people counting and reduces the incorrect elimination of the detection frames when heads are blocked by each other. Compared with the state-of-the-art YoloV4, the AP value of the proposed head detection method is increased from 88.52 to 90.54%. The Soft-YoloV4 model has much higher robustness and a lower missed detection rate for head detection, and therefore it dramatically improves the accuracy of people counting.

scholarly journals Vision Based Algorithm for People Counting Using Deep Learning

2018 ◽  
Vol 7 (3.6) ◽  
pp. 74
Author(s):  
M Padmashini ◽  
R Manjusha ◽  
Latha Parameswaran
Keyword(s):  
Image Processing ◽  
Deep Neural Networks ◽  
Local Binary Pattern ◽  
Support Vector ◽  
Shopping Malls ◽  
Energy Usage ◽  
People Detection ◽  
False Negatives ◽  
People Counting

Estimating the number of people in a particular scene has always been an important topic of research in computer vision and digital image processing. People counting has wide applications in scenario ranging from analyzing the customer's choice and improving the quality of service in retail stores, supermarkets and shopping malls to managing human resources and optimizing the energy usage in office buildings. While there exists algorithms for counting people in a scene, some algorithm have set their benchmark in performance with respect to efficiency, flexibility and accuracy. In this paper, an attempt has been made to perform people counting using Deep Neural Networks (DNN) on comparison with existing image processing based algorithms like Histogram of Oriented Gradients with Support Vector Machine (HoG with SVM), Local Binary Pattern (LBP) based Adaboost classifier and contour based people detection. The proposed DNN based approach has higher accuracy at 90% and less false negatives.  

The Effect of Memory Schemas on Object Recognition in Virtual Environments

2005 ◽  
Vol 14 (5) ◽  
pp. 606-615 ◽  
Author(s):  
Katerina Mania ◽  
Andrew Robinson ◽  
Karen R. Brandt
Keyword(s):  
Object Recognition ◽  
Computer Graphics ◽  
Scene Perception ◽  
Recognition Task ◽  
Theoretical Work ◽  
Head Tracking ◽  
Head Mounted Display ◽  
Inconsistent Objects ◽  
Memory Recognition

Prior theoretical work on memory schemas, an influential concept of memory from the field of cognitive psychology, is presented for application to fidelity of computer graphics simulations. The basic assumption is that an individual's prior experience will influence how he or she perceives, comprehends, and remembers new information in a scene. Schemas are knowledge structures; a scene could incorporate objects that fit into a specific context or schema (e.g., an academic's office) referred to as consistent objects. A scene could also include objects that are not related to the schema in place referred to as inconsistent objects. In this paper, we describe ongoing development of a rendering framework related to scene perception based on schemas. An experiment was carried out to explore the effect of object type and rendering quality on object memory recognition in a room. The computer graphics simulation was displayed on a Head Mounted Display (HMD) utilizing stereo imagery and head tracking. Thirty-six participants across three conditions of varied rendering quality of the same space were exposed to the computer graphics environment and completed a memory recognition task. Results revealed that schema consistent elements of the scene were more likely to be recognized than inconsistent information. Overall higher confidence ratings were assigned for consistent objects compared to inconsistent ones. Total object recognition was better for the mid-quality condition compared to the low-quality one. The presence of shadow information, though, did not affect recognition of either consistent or inconsistent objects. Further explorations of the effect of schemas on spatial awareness in synthetic worlds could lead to identifying areas of a computer graphics scene that require better quality of rendering as well as areas for which lower fidelity could be sufficient. The ultimate goal of this work is to simulate a perceptual process rather than to simulate physics.

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