ffmpeg  -i image%07d.png -s 640×480  -qscale 10 video.flv

As biometrics can now identify people based on individual measures, it appears prudent to translate these techniques for forensic use. As subjects can conceal features associated with identification, prior convictions have used gait and posture to identify suspect. The locations of human vertices are used within instantaneous posture matching. To derive a measure of confidence in this match, we use an automated analysis to determine the variation in the match measure as a function of increasing database size. We can match subjects between videos and assess the confidence in the match measure. We describe how we can derive a match for suspects recorded performing the same criminal act, in surveillance footage, and assess the confidence. As this is the first study of its kind, it raises many points to consider which can aid refinement not just of the matching procedure, but also constraints on the placement of cameras in surveillance.

We present a new method for view-point independent gait biometrics. The system relies on a single camera, does not require camera calibration and works with a wide range of camera-views. This is achieved by a formulation where the gait is self-calibrating. These properties make the proposed method particularly suitable for identification by gait, where the advantages of completely unobtrusiveness, remoteness and covertness of the biometric system preclude the availability of camera information and specific walking directions. Moreover, tests on the multi-view CASIA-B database, composed of more than 2270 video sequences with 65 different subjects walking freely along different walking directions have been performed. The obtained results show that human identification by gait can be achieved without any knowledge of internal or external camera parameters with a mean CCR of 73.6% using purely dynamic gait features. The performance of the proposed method is particularly encouraging for application in surveillance scenarios.

perl -pi.bak -e “s/pattern/new_string/g” *

http://www.wilsonweb.com/articles/checklist.htm

The simple command to use to convert a given video video.ext to a set of images preferably with the naming format XXXX.png is :

ffmpeg -i video.ext %4d.png

It will produce images with the naming : 0001.png 0002.png and so on. You can try different image format if needed ( Jpeg, bmp…)

In case you want to extract and convert only a specific portion of the video :

ffmpeg -ss hh:mm:ss -t hh:mm:ss -i video.ext %4d.png
-ss option is the starting point whilst -t option is the duration to be converted.

As trying to make a condition inside an SQL where clause based on the COUNT function or other functions, the trivial one is to use :

SELECT count(items) as cnt from user_items where cnt>3

This is not going to work, and therefore you need to use the following one instead :

SELECT count(items) as cnt from user_items having count(items)>3

In case you want to add a where clause to the SQL statement, here is how it should look :

SELECT count(items) as cnt from user_items where id>1000 having count(items)>3