Real-Time Illegal Parking Detection System Based on Deep Learning

The increasing illegal parking has become more and more serious. Nowadays the methods of detecting illegally parked vehicles are based on background segmentation. However, this method is weakly robust and sensitive to environment. Benefitting from deep learning, this paper proposes a novel illegal vehicle parking detection system. Illegal vehicles captured by camera are firstly located and classified by the famous Single Shot MultiBox Detector (SSD) algorithm. To improve the performance, we propose to optimize SSD by adjusting the aspect ratio of default box to accommodate with our dataset better. After that, a tracking and analysis of movement is adopted to judge the illegal vehicles in the region of interest (ROI). Experiments show that the system can achieve a 99% accuracy and real-time (25FPS) detection with strong robustness in complex environments.Comment: 5pages,6figure

Analysis of artificial neural network and viola-jones algorithm based moving object detection

In recent years, the worrying rate of street crime has demanded more reliable and efficient public surveillance system. Analysis of moving object detection methods is presented in this paper, includes Artificial Neural Network (ANN) and Viola-Jones algorithm. Both methods are compared based on their precision of correctly classify the moving objects. The emphasis is on two major issues involve in the analysis of moving object detection, and object classification to two groups, pedestrian and motorcycle. Experiments are conducted to quantitatively evaluate the performance of the algorithms by using two types of dataset, which are different in term of complexity of the background. The utilization of cascade architecture to the extracted features, benefits the algorithm. The algorithms have been tested on simulated events, and the more suitable algorithm with high detection rate is expected to be presented in this paper

Интеллектуальный видеоанализ опасных ситуаций

[For the English abstract and full text of the article please see the attached PDF-File (English version follows Russian version)].The work was supported by the Russian Foundation for Basic Research (Grant No. 17-20-03034). ABSTRACT The article is devoted to development of a system for the intelligent analysis of video recordings of external surveillance cameras, which makes it possible to identify dangerous situations at railway facilities using the example of detection of falls in the track area. A method of preprocessing a video for the purpose of forming a feature space based on the use of background subtraction using the Gaussian mixture method, followed by tracking the movement of a person with the help of the Kalman filter and deformation of the shape of the mobile object as a result of applying the procrustean analysis is proposed. The selection of the optimal composition of the feature space and additional heuristics providing the isolation of episodes of falls from video recording with an average quality of the Cohen’s kappa 0,62 is compared with the visual analysis by the operator. Keywords: railway, safety, video surveillance, intelligent video analysis, motion recognition, machine learning, form analysis.Текст аннотации на англ. языке и полный текст статьи на англ. языке находится в прилагаемом файле ПДФ (англ. версия следует после русской версии).Работа выполнена при поддержке Российского фонда фундаментальных исследований (грант № 17-20-03034). Статья посвящена разработке системы интеллектуального анализа видеозаписей камер наружного наблюдения, позволяющей выявлять опасные ситуации на объектах железных дорог на примере детекции падений в зоне пути. Предложен метод предобработки видеоряда с целью формирования пространства признаков, основанный на использовании вычитания фона по методу гауссовой смеси, последующем отслеживании перемещения человека при помощи фильтра Калмана и деформации формы подвижного объекта в результате применения прокрустова анализа. Обоснован подбор оптимального состава пространства признаков и дополнительных эвристик, обеспечивающих выделение эпизодов падений по видеозаписи со средним качеством каппы Коэна 0,62 по сравнению с визуальным анализом оператором

AEIS: An Enhanced Approach for Extracting Useful Objects in Image Streams

المصطلح عنقدة البيانات المستمرة يشير الى عملية توزيع مستمرة للبيانات الجديدة والمتولدة بشكل مستمر الى مجاميع قابلة للتغيير بشكل مستمر لتمكين عملية التحليل المتزامنة للانماط الجديدة. على اية حال، توجه البحوث في مجال خوارزميات العنقدة الى وقتنا هذا متركزة على تحديث هذه الخوارزميات والتي تعمل مع البيانات الثابتة الى بيئة البيانات المستمرة او تطوير خوارزميات البيانات المستمرة. هذا البحث يقدم خوارزمية تجميع جديدة تدعى AEIS والتي تميز التواجدات الثابتة في الطور المباشر وكذلك تميز العناقيد في الطور غير المباشر للصور المستمرة. في هذا البحث، تم تقديم طريقة جديدة لايجاد وتحديد التواجدات المتمثلة بالصور المرسلة من المصدر سواء اكان كامرا او متحسس. التواجدات المستمرة هي تلك التواجدات التي من الممكن ان يتم فصلها عن خلفية الصورة المرسلة وتبقى بدون تغيير لفترة طويلة. استخلاص التواجدات الثابتة من الصور المرسلة له اهمية كبيرة في عملية اكتشاف بعض الاشياء المبهمة مثلا مراقبة الحقائب المشبوهه في المطارات والاماكن العامة، متابعة المركبات والصواريخ المرسلة الى الفضاء،متابعة حضائر الحيوانات، وتحليل الموجات المنعكسة من اعماق البحار والمحيطات. الخوارزمية المقترحة تعتمد على تحديد مناطق في الصورة المرسلة بتحديد البكسل التابعة لها ومقارنتها مع ظهور/اختفاء التواجدات. تقوم الخوارزمية في المرحلة الاولى بتحديد مجموعة البكسل التي تعود للتواجدات واختبارها باستخدام نموذج النظام. بعد ذلك، المجاميع من البكسل المميزة باللون الاسود يتم استخلاصها من الصورة. هذه هي طريقة استخلاص وتحديد التواجدات الثابتة. نتائج الخوارزمية المقترحة من الممكن ان يتم تحليلها باستخدام المصنفات، مختلف التواجدات المحددة عن بقية التواجدات، ونظام تحديد القرار لاكتشاف الاشياء غير المتوقعة. التركيز الرئيسي لهذا البحث هو على خوارزميات استخلاص المناطق المميزة من الصورة. على اية حال، الهيكل الكامل لاكتشاف التواجدات غير المتوقعة كذلك تم تقديمه في سبيل ان اكتشاف صحيح وحقيقي للاحداث. هذه الخوارزمية تم فحصها على بيانات افتراضية لغرض معرفة مدى فاعليتها.  النتائج النهائية للتجارب وثقت فاعلية وفائدة الخوارزمية المقترحة ومدى فرقها عن سابقاتهاData stream clustering alludes to the way toward gathering persistently arriving new information pieces into consistently changing gatherings to empower dynamic examination of division designs. Be that as it may, the fundamental consideration of research on clustering techniques till now has been worried about modification of the strategies refreshed for static datasets and changes of the accessible adjusted techniques. This paper shows a novel clustering (AEIS) method that distinguish stationary articles in online stage and gatherings in disconnected stage from information stream. A tale way to deal with discovery of stationary objects in the information streams is introduced. Stationary objects are these isolated from the static foundation, yet staying unmoving for a drawn out time. Extraction of stationary objects from images is helpful in programmed location of numerous applications, for example, unattended baggage, following an article like creatures, rockets, or reflected waves. The proposed method depends on discovery of image districts containing forefront picture pixels having stable qualities in time and checking their correspondence with the distinguished showing up/vanishing objects. In the primary phase of the method, steadiness of individual pixels having a place with items is tried utilizing a built model. Next, groups of pixels with stable shading (black) are separated from the image. Along these lines, stationary objects are recognized. The consequences of the calculation might be examined further by the classifier, isolating explicit objects (like baggage, creatures, rockets) from different objects, and the choice framework for unattended items identification. The primary focal point of the paper is on the method for extraction of stable image areas. Notwithstanding, a total structure for unattended objects location is likewise exhibited so as to demonstrate that the proposed methodology gives information to fruitful occasion identification. The aftereffects of tests wherein the proposed method was approved utilizing synthesised dataset are exhibited and talked about

A computational system to monitor and control animal behaviour during perceptual tasks

En col·laboració amb la Universitat de Barcelona (UB) i la Universitat Rovira i Virgili (URV)In the neuroscience field the scientists aim to understand how the brain works. In order to study the brain mechanisms underlying behaviour and cognition, they perform standardized laboratory experiments with animal models. The main goal of this Master Thesis is the development of an experimental set-up to run behavioral experiments using rats in the DeLaRocha Lab at IDIBAPS (Institut d’Investigacions Biomédiques August Pi i Sunyer) of Barcelona. Here there are many people that are studying the brain and its features. Every day the researchers makes hypothesis and try to demonstrate it. In order to perform that, it is necessary to make many experiments. Therefore, a system that can contain the rat, run the task and obtain results is needed. Moreover, it has to control the task in an automatized way, to show in real-time some useful information about the running task to the user and to save all the data in the proper format to be read easily afterwords. The entire system is programmed using Python and an Arduino boards to communicate with the experimental devices, i.e. water valves, lights, speakers and camera. The system monitors in real time and in a quantitative manner the behavior of the animal and serves as an interface to the experimenter to assess performance, statistic about responses, etc. To make everything works the system has to be fast (real-time) in terms of communication between the hardware, response of the devices and visualization of the data. All these parts are organized to work together. The majority of the experiments are based on Two Alternative Forced-Choice (2AFC) tasks. In 2AFC, the subject receives a stimulus and after that, two alternatives are presented. Only one is the correct choice. Normally, a reward or a punishment are used after the decision, depending on the choice (this strategy is also called Reinforcement Learning). Therefore, the environment of the experiment has three ports: left, central and right. The right and left one are used as alternatives while the center one is used to get the task starts. Each of them has a infra-red beams to detect when the rats pokes in/out, a LED that can be turn on/off and a metal tube for the water delivery as reward. Furthermore, there are two speakers, through which the sound stimuli are delivered, and a big light that turns-on as a punishment for a wrong choice. All the components are controlled as Finite State-Machines by the Arduino board. It means that the states and the transitions are defined by external input, e.g. by the computer. The latter is connected to the Arduino board that controls the devices, to a camera that records the experiments, and to a sound card to trig the stimulus. All these components need to work jointly. This Master Thesis will include the development of a video tracking system, a feature of capital importance for certain studies, that has been missing in the previous system’s that have been used at the laboratory. The algorithm is specifically designated and developed for these kind of experiments with rats. It is useful to tracks the head during the tasks for many application such as to detect when a “Change of Mind” occurs. Many approaches of foreground subtraction are exposed and commented. Then a novel adaptiveselective background updating method is proposed to avoid some issue where other methods fail. Afterwords, the method is used to track the position of the rat. Finally, the algorithm is compared with the others methods in terms of general problem of foreground detection. Then it is tested comparing the tracking with the experiments results of a real task to obtain a measure of accuracy and precision of this method. All the details of the behaviour boxes where rats perform the tasks, the system that controls the experiment and the video analysis are explained step by step in this Master Thesis

Stopped Object Detection by Learning Foreground Model in Videos

The automatic detection of objects that are abandoned or removed in a video scene is an interesting area of computer vision, with key applications in video surveillance. Forgotten or stolen luggage in train and airport stations and irregularly parked vehicles are examples that concern significant issues, such as the fight against terrorism and crime, and public safety. Both issues involve the basic task of detecting static regions in the scene. We address this problem by introducing a model-based framework to segment static foreground objects against moving foreground objects in single view sequences taken from stationary cameras. An image sequence model, obtained by learning in a self-organizing neural network image sequence variations, seen as trajectories of pixels in time, is adopted within the model-based framework. Experimental results on real video sequences and comparisons with existing approaches show the accuracy of the proposed stopped object detection approach

A Novel Multi-View Table Tennis Umpiring Framework

This research investigates the development of a low-cost multi-view umpiring framework, as an alternative to the current expensive systems that are almost exclusively restricted to elite professional sports. Table tennis has been selected as the testbed because, while automating the process is challenging, it has many different complex match elements including the service, return and rallies, which are governed by a strict set of regulations. The focus is mainly on the rally element rather than the whole match. Ball detection and tracking in video frames are undertaken to determine reliably the ball position relative to key reference objects like the table surface and net, and the ball’s flight path is used to determine the rally’s status. While a low-cost option has benefits, it is technically challenging due to the limited number of cameras and generally low video resolution used. This thesis presents a portable multi-view umpiring framework that identifies each state change in a rally. It makes three significant contributions to knowledge: i) a reliable ball detection strategy that accurately detects the location of the ball in low-resolution sequences; ii) a novel framework for ball tracking using a multi-view system, and iii) a new state-machine based evaluation system for analysing table tennis rallies. In a series of ten different test scenarios, the system achieved an average of 94% system detection rate and 100% accurate decisions. A test sequence of duration 1 s can be processed in 8 s, leading to a delay of only 7 s, which is considered acceptable for practical purposes. This solution has the potential to reform the way matches are umpired, providing objectivity in resolving disputed decisions. It affords an economic technology for amateur players, while the multi-view facility is extendible to other relevant ball-based sports. Finally, the ball flight path analysis mechanism can be a valuable training tool for skills development