The problem Gait Recognition for Identifying Chain Snatchers solves

The problem Gait Recognition for Identifying Chain Snatchers solves
The problem Gait Recognition for Identifying Chain Snatchers solves
We suffer from various offences by reckless people who involve themselves in unlawful acts such as theft, abducting the children and molesting the women. To put an end for these ruthless acts we use this Gait recognition technique to pin out the burglars from the CCTV images though the face of the person is concealed. Nowadays in this multi developed society, CCTV cameras are installed in almost all the places of the cities as it have become one among the essential needs to prevent and as well as sort out the thieves easily. But the delinquents by far escape by shielding their faces. Inorder to catch the thieves, we come out with an idea using machine learning algorithms which helps to identify the people even if their faces were concealed. Gait recognition is the technique which spots the individual through silhouette images. The main lead of Gait recognition is that the person can also be recognized even his/her face is masked or hidden. The gait recognition is not restricted to a single camera captured image but also can also be initiated through images from different cameras. This technique would stand as a boon to sort out the offenders who unlawfully indulge themselves in various criminal activities such as theft, abducting the children and molesting the women.

Challenges we ran into

Challenges we ran into
bwcovhull error - Error came because of a variable change
bwcovhull error <parseInt Error - Error because of parenthesis parameters
Undefined function knn classify for input arguments of type "cell" - The most toughest error we faced and it took nearly 7 hours to solve