#SUSPICIOUS BEHAVIOR WINDOWS#

The most important thing to remember is that you use your instinct. There is no way to completely define all suspicious activity. These are just a few examples of some suspicious activity that could occur in your presence. Unusual noises such as gunshots, screaming, abnormally barking dogs, breaking glass, or any noise that is suggestive of foul play.

#SUSPICIOUS BEHAVIOR WINDOWS#

Open or broken windows at closed businesses or residences whose owners are temporarily absent.

Continuous traffic that comes and goes to the same location, usually staying for very short periods of time.

Continuous 'Repair' operations at a non-business location.

A non-resident going into a back or side yard.

Someone forcing entrance to or tampering with a residence or business.

Someone waiting in front of a house or business when the owners are absent, or if it is a business and the establishment is closed.

Someone going door-to-door in a residential area if after a few houses are visited, one or more of the subjects goes into a back or side yard.

Suspicious Behavior Involving Homes or Businesses

(Several televisions being stored in a garage for a long period of time.)

Items that accumulate in private garages, storage areas, or on property if it accumulates, especially if the items are in good condition but not in use.

Someone offers to sell you something for significantly less than the market value.

Property being removed from or being placed into vehicles or buildings if removed from closed residences whose owners are known to be absent.

Especially questionable if the person is running.

Persons being forced into vehicles, especially if juveniles or females.

Apparent business transactions conducted from a vehicle especially if around schools or parks, and if juveniles are involved.

Persons detaching mechanical parts or accessories from a vehicle especially at night or in a parking lot.

Persons attempting forcibly to enter a locked vehicle, especially at night or in a parking lot.

Persons pulling door handles on parked vehicles.

Persons looking into windows of parked vehicles.

True, even if occupants appear to be lovers. May contain one or more persons, especially significant if observed at an unusual hour. This is true even if the vehicle is a legitimate looking commercial unit.

Vehicles being loaded with valuables are suspicious if parked in front of a closed business or untended residence.

This is suspicious in any location, but particularly in areas of schools, parks, or playgrounds.Ĭould possibly be a newspaper delivery person - If so, please don’t call!.

Slow moving vehicles, vehicles without lights, and/or the course followed appears aimless or repetitive.

9 am until 3 pm on any Monday, Tuesday, Wednesday, Thursday or Friday on days that the school in which the minor is enrolled is in session unless the minor has school permission to be off-campus.

1:01 am until 6 am on any Saturday or Sunday.

12:01 am on any Monday, Tuesday, Wednesday, Thursday, or Friday until 6 am of that same day.

Loitering in neighborhoods they are not from.

The results show that many of the pickpockets can be detected at a low false alarm rate. We estimate the quality of these features and the performance of automatic detection of (collaborating) pickpockets. In this paper, we describe our method, identify the most valuable features, and analyze the results that were obtained in the experiment. We performed a cross validation to train and evaluate our system. The classifier was used to separate the pickpockets from normal passers-by who are shopping in the mall. We used a top-down approach to translate expert knowledge in features and rules, and a bottom-up approach to learn discriminating patterns with a classifier. We carried out an experiment with more than 20 validated pickpocket incidents. This is challenging because the environment is crowded, people move freely through areas which cannot be covered by a single camera, because the actual snatch is a subtle action, and because collaboration is complex social behavior. Our method consists of several steps: pedestrian tracking, feature computation and pickpocket recognition.

This paper focusses on the automatic early detection of suspicious behavior of pickpockets with track-based features in a crowded shopping mall. Proactive detection of incidents is required to decrease the cost of security incidents. Automatic detection of suspicious behavior of pickpockets with track-based features in a shopping mall

Suspicious behavior