title: ÐÐ±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ Ð°Ð½Ð¾Ð¼Ð°Ð»ÑÐ½Ð¾Ð¹ Ð°ÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ Ð¿Ð¾ ÐºÐ°Ð´ÑÐ°Ð¼ Ð²Ð¸Ð´ÐµÐ¾Ð½Ð°Ð±Ð»ÑÐ´ÐµÐ½Ð¸Ñ Ð¼Ð½Ð¾Ð³Ð¾Ð»ÑÐ´Ð½ÑÑ ÑÑÐµÐ½
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Abnormal activities detection in crowded scenes using video surveillance
Creators: name: ÐÑÑÐ¸, ÐÐ°ÑÑÐ¸Ð¼
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2019
Subjects: ÐÐ¸Ð´ÐµÐ¾ÑÐµÑ Ð½Ð¸ÐºÐ°
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Ð²Ð¸Ð·ÑÐ°Ð»ÑÐ½Ð¾Ðµ Ð½Ð°Ð±Ð»ÑÐ´ÐµÐ½Ð¸Ðµ
ÑÐ¸ÑÑÐµÐ¼Ñ ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ð³Ð¾ Ð²Ð¸Ð´ÐµÐ½Ð¸Ñ

description: ÐÐ±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ Ð°Ð½Ð¾Ð¼Ð°Ð»ÑÐ½Ð¾Ð¹ Ð°ÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ ÑÐ²Ð»ÑÐµÑÑÑ ÑÐ»Ð¾Ð¶Ð½Ð¾Ð¹ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ð¾Ð¹ Ð² Ð²Ð¸Ð´ÐµÐ¾Ð½Ð°Ð±Ð»ÑÐ´ÐµÐ½Ð¸Ð¸ Ð² ÑÐµÐ»ÑÑ Ð±ÐµÐ·Ð¾Ð¿Ð°ÑÐ½Ð¾ÑÑÐ¸ Ð¸ Ð·Ð°ÑÐ¸ÑÑ ÑÐ¸ÑÑÐµÐ¼Ñ. ÐÑ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÑÐµÐ¼ Ð½Ð¾Ð²ÑÐ¹ Ð¿Ð¾Ð´ÑÐ¾Ð´ Ðº Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð°Ð½Ð¾Ð¼Ð°Ð»ÑÐ½Ð¾Ð¹ Ð°ÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ Ð¿ÑÐ¸ Ð²Ð¸Ð´ÐµÐ¾Ð½Ð°Ð±Ð»ÑÐ´ÐµÐ½Ð¸Ð¸ Ñ Ð¿ÐµÑÐµÐ¿Ð¾Ð»Ð½ÐµÐ½Ð½ÑÐ¼Ð¸ ÑÑÐµÐ½Ð°Ð¼Ð¸, ÐºÐ¾ÑÐ¾ÑÑÐ¹ Ð²Ð°Ð¶ÐµÐ½ Ð´Ð»Ñ Ð¼Ð½Ð¾Ð³Ð¸Ñ Ð¿Ð¾Ð²ÑÐµÐ´Ð½ÐµÐ²Ð½ÑÑ ÑÐµÐ»ÐµÐ¹. ÐÐ°Ñ Ð¿Ð¾Ð´ÑÐ¾Ð´ Ð¾ÑÐ½Ð¾Ð²Ð°Ð½ Ð½Ð° ÑÑÐµÑ ÑÐ¸Ð¿Ð°Ñ ÑÑÐ½ÐºÑÐ¸Ð¹ Ð´Ð»Ñ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð°Ð½Ð¾Ð¼Ð°Ð»ÑÐ½ÑÑ ÑÐ¾Ð±ÑÑÐ¸Ð¹. ÐÐµÑÐ²ÑÐ¹ ÑÐ¸Ð¿ - ÑÑÐ¾ Ð¾ÑÐ¾Ð±ÐµÐ½Ð½Ð¾ÑÑÐ¸ Ð´Ð²Ð¸Ð¶ÐµÐ½Ð¸Ñ, Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½ÑÐµ Ð½Ð° ÑÐ°ÑÑÐµÑÐµ Ð¾Ð¿ÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð¿Ð¾ÑÐ¾ÐºÐ°, Ð²ÑÐ¾ÑÐ¾Ð¹ ÑÐ¸Ð¿ - ÑÑÐ¾ ÑÐ°Ð·Ð¼ÐµÑ Ð´Ð²Ð¸Ð¶ÑÑÐ¸ÑÑÑ Ð»ÑÐ´ÐµÐ¹ Ð²Ð´Ð¾Ð»Ñ ÐºÐ°Ð´ÑÐ¾Ð², Ð° ÑÑÐµÑÐ¸Ð¹ ÑÐ¸Ð¿ - ÑÑÐ¾ Ð²ÐµÐ»Ð¸ÑÐ¸Ð½Ð° Ð´Ð²Ð¸Ð¶ÐµÐ½Ð¸Ñ. ÐÑ Ð±ÑÐ´ÐµÐ¼ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÑ Ð¼Ð½Ð¾Ð³Ð¾ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð¾Ð² Ðº Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð² Ð¾Ð±ÑÑÐµÐ½Ð¸Ð¸ Ð¿Ð¾Ð´ Ð½Ð°Ð±Ð»ÑÐ´ÐµÐ½Ð¸ÐµÐ¼ Ð¸ Ð¾Ð±ÑÑÐµÐ½Ð¸ÐµÐ¼ Ð¿Ð¾Ð´ Ð½Ð°Ð±Ð»ÑÐ´ÐµÐ½Ð¸ÐµÐ¼ Ð¸ Ð±ÐµÐ· Ð½ÐµÐ³Ð¾, ÑÑÐ¾Ð±Ñ Ð²ÑÐ±ÑÐ°ÑÑ Ð»ÑÑÑÐ¸Ð¹ Ð¼ÐµÑÐ¾Ð´. ÐÑ Ð±ÑÐ´ÐµÐ¼ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÑ Ð½Ð°Ð±Ð¾Ñ Ð´Ð°Ð½Ð½ÑÑ UCSD Anomaly Detection. ÐÐ°ÑÐµÐ¹ ÑÐµÐ»ÑÑ ÑÐ²Ð»ÑÐµÑÑÑ Ð²ÑÑÐ²Ð»ÐµÐ½Ð¸Ðµ Ð°Ð½Ð¾Ð¼Ð°Ð»Ð¸Ð¹ Ð² Ð²Ð¸Ð´ÐµÐ¾Ð¿Ð¾ÑÐ¾ÐºÐ°Ñ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¸ Ð¼Ð½Ð¾Ð³Ð¸Ñ ÑÐ¸Ð¿Ð¾Ð² Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ñ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾Ð³Ð¾ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ°, Ð¾ÑÐµÐ½ÐºÐ¸ Ð¸ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ñ Ð¼ÐµÐ¶Ð´Ñ ÑÑÐ¸Ð¼Ð¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð°Ð¼Ð¸ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¼Ð½Ð¾Ð¶ÐµÑÑÐ²Ð° Ð½Ð°Ð±Ð¾ÑÐ¾Ð² Ð´Ð°Ð½Ð½ÑÑ. Ð ÑÑÐ¾Ð¼ Ð¾ÑÑÐµÑÐµ Ð¼Ñ Ð¸Ð¼ÐµÐµÐ¼ Ð´Ð»Ñ Ð¾ÑÐ½Ð¾Ð²Ð½ÑÑ Ð³Ð»Ð°Ð², Ð² Ð¿ÐµÑÐ²Ð¾Ð¹ Ð³Ð»Ð°Ð²Ðµ Ð¾Ð¿Ð¸ÑÑÐ²Ð°ÐµÑÑÑ Ð½Ð°ÑÐ° Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ð°, Ð¸ Ð´Ð»Ñ ÑÐµÐ³Ð¾ Ð¼Ñ ÑÐµÑÐ°ÐµÐ¼ ÑÑÑ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ñ, Ð¾Ð±ÑÑÑÐ½ÑÐµÐ¼ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ñ Ð¸ Ð¿Ð¾Ð´ÑÐ¾Ð´Ñ, Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð½ÑÐµ Ð² Ð¿ÑÐµÐ´ÑÐ´ÑÑÐ¸Ñ ÑÐ°Ð±Ð¾ÑÐ°Ñ Ð² ÑÐ¾Ð¹ Ð¶Ðµ Ð¾Ð±Ð»Ð°ÑÑÐ¸. ÐÐ¾ Ð²ÑÐ¾ÑÐ¾Ð¹ Ð³Ð»Ð°Ð²Ðµ Ð¼Ñ Ð¿Ð¾Ð³Ð¾Ð²Ð¾ÑÐ¸Ð¼ Ð¾ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð¼ Ð¾Ð±ÑÑÐµÐ½Ð¸Ð¸ Ð¸ Ð¾ ÑÐ¾Ð¼, ÑÑÐ¾ Ð¼Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÐ¼ Ð´Ð»Ñ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ñ Ð°Ð½Ð¾Ð¼Ð°Ð»ÑÐ½ÑÑ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ¾Ð² Ð°ÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸. Ð ÑÑÐµÑÑÐµÐ¼ Ð¼Ñ Ð¾Ð¿Ð¸ÑÑÐ²Ð°ÐµÐ¼ Ð½Ð°Ñ Ð²ÐºÐ»Ð°Ð´ Ð¸ ÑÐ¾, ÐºÐ°Ðº Ð¼Ñ ÑÐµÑÐ°ÐµÐ¼ ÑÑÑ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ñ. Ð Ð¿Ð¾ÑÐ»ÐµÐ´Ð½ÐµÐ¹ Ð³Ð»Ð°Ð²Ðµ Ð¼Ñ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÑÐµÐ¼ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¸ ÑÑÐ°Ð²Ð½Ð¸Ð²Ð°ÐµÐ¼ Ð¸Ñ, ÑÑÐ¾Ð±Ñ Ð²ÑÐ±ÑÐ°ÑÑ Ð»ÑÑÑÐ¸Ð¹ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾Ñ.
description: Abnormal activity detection is a challenging problem in video surveillance for security purposes and protection system. We presents a new approach for abnormal activity detection in videos surveillance with crowded scenes which is important for many daily purposes. Our approach based on three types of features for detecting anomaly events. First type is motion features based on optical flow calculation, second type is the size of moving individuals along frames, and third type is motion magnitude. We will use many training approaches in supervised and semi-supervised and unsupervised learning to choose the best method. We will use UCSD Anomaly Detection dataset. Our aim is to identify anomalies in video feeds using Machine Learning techniques and many types of learning algorithms to build an effective classifier, evaluate and compare between these algorithms using many datasets. In this report we have for main chapters, first chapter introduce our problem and what for we solve this problem, explain the challenges and approaches used in previous works in the same field. In the second chapter we talk about machine learning and what we use to build abnormal activity classifiers. In the third one, we describe our contribution and how we process this problem. In the final chapter we present the results and compare between them to choose the best classifier.