Now a days, Closed Circuit Television (CCTV) cameras are installedeverywhere in public places to monitor illegal activities like armedrobberies. Mostly CCTV footages are used as post evidence after theoccurrence of crime. In many cases a person might be monitoringthe scene from CCTV but the attention can easily drift on prolongedobservation. Eciency of CCTV surveillance can be improved by in-corporation of image processing and object detection algorithms intomonitoring process.The object detection algorithms, previously implemented in CCTVvideo analysis detect pedestrians, animals and vehicles. These algo-rithms can be extended further to detect a person holding weaponslike rearms or sharp objects like knives in public or restricted places.In this work the detection of weapon from CCTV frame is acquiredby using Histogram of Oriented Gradients (HOG) as feature vector andarticial neural networks performing back-propagation algorithm forclassication.As a weapon in the hands of a human is considered to be greaterthreat as compared to a weapon alone, in this work the detection ofhuman in an image prior to a weapon detection has been found advan-tageous. Weapon detection has been performed using three methods.In the rst method, the weapon in the image is detected directly with-out human detection. Second and third methods use HOG and back-ground subtraction methods for detection of human prior to detectionof a weapon. A knife and a gun are considered as weapons of inter-est in this work. The performance of the proposed detection methodswas analysed on test image dataset containing knives, guns and im-ages without weapon. The accuracy rate 84:6% has been achievedby a single-class classier for knife detection. A gun and a knife havebeen detected by the three-class classier with an accuracy rate 83:0%.

Att använda maskininlärning för att detektera vapen eliminerar en konstant mänsklig övervakning, vilket också kan leda till en lägre responstid till polis. I den här rapporten undersöks hur en vapendetektor kan konstrueras och byggas som en del av en klient-server-lösning. / With the progress of deep learning methods the last couple of years, object detectionrelated tasks are improving rapidly. Using object detection for detecting guns in schoolsremove the need for human supervision and hopefully reduces police response time. Thispaper investigates how a gun detection system can be built by reading frames locally andusing a server for detection. The detector is based on a pre-trained SSD model and throughtransfer learning is taught to recognize guns. The detector obtained an Average Precisionof 51.1% and the server response time for a frame of size 1920 x 1080 was 480 ms, but couldbe scaled down to 240 x 135 to reach 210 ms, without affecting the accuracy. A non-gunclass was implemented to reduce the number of false positives and on a set of 300 imagescontaining 165 guns, the number of false positives dropped from 21 to 11.

With the progress of deep learning methods the last couple of years, object detection related tasks are improving rapidly. Using object detection for detecting guns in schools remove the need for human supervision and hopefully reduces police response time. This paper investigates how a gun detection system can be built by reading frames locally and using a server for detection. The detector is based on a pre-trained SSD model and through transfer learning is taught to recognize guns. The detector obtained an Average Precision of 51.1% and the server response time for a frame of size 1920 x 1080 was 480 ms, but could be scaled down to 240 x 135 to reach 210 ms, without affecting the accuracy. A non-gun class was implemented to reduce the number of false positives and on a set of 300 images containing 165 guns, the number of false positives dropped from 21 to 11.