A Method for Counting People in Crowded Scenes

This paper presents a novel method to count people for video surveillance applications. Methods in the literature either follow a direct approach, by first detecting people and then counting them, or an indirect approach, by establishing a relation between some easily detectable scene features and the estimated number of people. The indirect approach is considerably more robust, but it is not easy to take into account such factors as perspective or people groups with different densities. The proposed technique, while based on the indirect approach, specifically addresses these problems; furthermore it is based on a trainable estimator that does not require an explicit formulation of a priori knowledge about the perspective and density effects present in the scene at hand. In the experimental evaluation, the method has been extensively compared with the algorithm by Albiol et al., which provided the highest performance at the PETS 2009 contest on people counting. The experimentation has used the public PETS 2009 datasets. The results confirm that the proposed method improves the accuracy, while retaining the robustness of the indirect approach

Аналіз натовпу людей із застосуванням методів комп’ютерного зору

Розглянуто три важливі проблеми аналізу натовпу: підрахунок людей/оцінка щільності, відстежування в сценах з натовпом людей та розуміння поведінки натовпу. Автоматизоване оцінювання і підрахунок щільності натовпу – актуальна і важлива тема в аналізі натовпу. В статті представлено огляд оцінки щільності натовпу, методи її підрахунку, а також методи відстежування натовпу і розуміння поведінки груп людей. Огляд охоплює два основні підходи, а саме: прямий і непрямий.Рассмотрены три важные проблемы анализа толпы: подсчет людей/оценка плотности, отслеживание в сценах с толпой людей и понимания поведения толпы. Автоматизированное оценивание и подсчет плотности толпы – это актуальная и важная тема в анализе толпы. Обзор охватывает два основных подхода оценки плотности толпы, а именно: прямой и косвенный.Three important problems in crowd analysis are considered in the paper: people counting/density estimation, tracking in crowd scenes, and understanding crowd behavior in higher-level analysis Automated crowd density estimation and crowd counting are actual and important topics in crowd analysis. The article provides an overview of crowd density estimation, methods for calculating crowd density, and methods for tracking crowds and understanding the behavior of groups of people. This review covers two main approaches, direct and indirect

A Recent Trend in Individual Counting Approach Using Deep Network

In video surveillance scheme, counting individuals is regarded as a crucial task. Of all the individual counting techniques in existence, the regression technique can offer enhanced performance under overcrowded area. However, this technique is unable to specify the details of counting individual such that it fails in locating the individual. On contrary, the density map approach is very effective to overcome the counting problems in various situations such as heavy overlapping and low resolution. Nevertheless, this approach may break down in cases when only the heads of individuals appear in video scenes, and it is also restricted to the feature’s types. The popular technique to obtain the pertinent information automatically is Convolutional Neural Network (CNN). However, the CNN based counting scheme is unable to sufficiently tackle three difficulties, namely, distributions of non-uniform density, changes of scale and variation of drastic scale. In this study, we cater a review on current counting techniques which are in correlation with deep net in different applications of crowded scene. The goal of this work is to specify the effectiveness of CNN applied on popular individuals counting approaches for attaining higher precision results

A Review of Hybrid Indoor Positioning Systems Employing WLAN Fingerprinting and Image Processing

Location-based services (LBS) are a significant permissive technology. One of the main components in indoor LBS is the indoor positioning system (IPS). IPS utilizes many existing technologies such as radio frequency, images, acoustic signals, as well as magnetic sensors, thermal sensors, optical sensors, and other sensors that are usually installed in a mobile device. The radio frequency technologies used in IPS are WLAN, Bluetooth, Zig Bee, RFID, frequency modulation, and ultra-wideband. This paper explores studies that have combined WLAN fingerprinting and image processing to build an IPS. The studies on combined WLAN fingerprinting and image processing techniques are divided based on the methods used. The first part explains the studies that have used WLAN fingerprinting to support image positioning. The second part examines works that have used image processing to support WLAN fingerprinting positioning. Then, image processing and WLAN fingerprinting are used in combination to build IPS in the third part. A new concept is proposed at the end for the future development of indoor positioning models based on WLAN fingerprinting and supported by image processing to solve the effect of people presence around users and the user orientation problem

Effective evaluation of privacy protection techniques in visible and thermal imagery

Privacy protection may be defined as replacing the original content in an image region with a new (less intrusive) content having modified target appearance information to make it less recognizable by applying a privacy protection technique. Indeed the development of privacy protection techniques needs also to be complemented with an established objective evaluation method to facilitate their assessment and comparison. Generally, existing evaluation methods rely on the use of subjective judgements or assume a specific target type in image data and use target detection and recognition accuracies to assess privacy protection. This paper proposes a new annotation-free evaluation method that is neither subjective nor assumes a specific target type. It assesses two key aspects of privacy protection: protection and utility. Protection is quantified as an appearance similarity and utility is measured as a structural similarity between original and privacy-protected image regions. We performed an extensive experimentation using six challenging datasets (having 12 video sequences) including a new dataset (having six sequences) that contains visible and thermal imagery. The new dataset is made available online for community. We demonstrate effectiveness of proposed method by evaluating six image-based privacy protection techniques, and also show comparisons of proposed method over existing methods

Unsupervised Methods for Camera Pose Estimation and People Counting in Crowded Scenes

Most visual crowd counting methods rely on training with labeled data to learn a mapping between features in the image and the number of people in the scene. However, the exact nature of this mapping may change as a function of different scene and viewing conditions, limiting the ability of such supervised systems to generalize to novel conditions, and thus preventing broad deployment. Here I propose an alternative, unsupervised strategy anchored on a 3D simulation that automatically learns how groups of people appear in the image and adapts to the signal processing parameters of the current viewing scenario. To implement this 3D strategy, knowledge of the camera parameters is required. Most methods for automatic camera calibration make assumptions about regularities in scene structure or motion patterns, which do not always apply. I propose a novel motion based approach for recovering camera tilt that does not require tracking. Having an automatic camera calibration method allows for the implementation of an accurate crowd counting algorithm that reasons in 3D. The system is evaluated on various datasets and compared against state-of-art methods

Estudio de los efectos de perspectiva en contadores de personas basados en vídeo con lentes de gran angular

Los Sistemas de Conteo de Personas han cobrado gran importancia en los últimos años debido a que las aplicaciones en las que participan tienen repercusión económica y social. La mayoría de los sistemas modernos de conteo (aquellos que utilizan la Visión por Computador) alcanzan muy buenas precisiones en diferentes escenarios, y esto se refleja en varios trabajos previos. Sin embargo, ningún estudio completo se ha realizado utilizando cámaras con gran angular y colocados a baja altura, ni se ha planteado cómo los efectos causados por estos afectan a la calidad del conteo. Además, la mayoría de los sistemas utilizan un Entrenamiento Supervisado, lo que implica un coste de tiempo y trabajo manual adicional para obtener estos datos. Este trabajo tiene como objetivo analizar todos los escenarios descritos anteriormente y proponer un método estadístico que implique un Entrenamiento No Supervisado, con el fin de que el sistema tenga un auto-aprendizaje inteligente y alcance buenas precisiones de conteo.Mera Gutierrez, JI. (2014). Estudio de los efectos de perspectiva en contadores de personas basados en vídeo con lentes de gran angular. http://hdl.handle.net/10251/46298.Archivo delegad

Automatic counting of mounds on UAV images using computer vision and machine learning

Site preparation by mounding is a commonly used silvicultural treatment that improves tree growth conditions by mechanically creating planting microsites called mounds. Following site preparation, an important planning step is to count the number of mounds, which provides forest managers with an estimate of the number of seedlings required for a given plantation block. In the forest industry, counting the number of mounds is generally conducted through manual field surveys by forestry workers, which is costly and prone to errors, especially for large areas. To address this issue, we present a novel framework exploiting advances in Unmanned Aerial Vehicle (UAV) imaging and computer vision to estimate the number of mounds on a planting block accurately. The proposed framework comprises two main components. First, we exploit a visual recognition method based on a deep learning algorithm for multiple object detection by pixel-based segmentation. This enables a preliminary count of visible mounds and other frequently seen objects on the forest floor (e.g., trees, debris, accumulation of water) to be used to characterize the planting block. Second, since visual recognition could be limited by several perturbation factors (e.g., mound erosion, occlusion), we employ a machine learning estimation function that predicts the final number of mounds based on the local block properties extracted in the first stage. We evaluate the proposed framework on a new UAV dataset representing numerous planting blocks with varying features. The proposed method outperformed manual counting methods in terms of relative counting precision, indicating that it has the potential to be advantageous and efficient under challenging situations