Q:

KLT Tracker for human tracking in CCTV

I am trying to use a KLT tracker for human tracking in a CCTV footage. The people are very close to the CCTV. I noticed that some time people change the orientation of the heads and also the frame rate is slightly slow. I have read from Rodrigues et al. paper Section 3.4 that the:
"This simple procedure (KLT tracking procedure) is extremely robust and can establish matches between head detections where the head HAS NOT BEEN continuously detected continuously detected due to pose variation or partial occlusions due to other members of the crowd".
Paper can be found in this link : Rodriguez et al.
1). I understood that the KLT tracker is robust to pose variations and occlusions. Am I right?
I was trying to track one single person in footage till now by using the MATLAB KLT as in :
MATLAB KLT
However, the points were not being found after JUST 3 frames.  
2). Can someone explain why this is happening or else a better solution to this. Maybe using a particle/Kalman filter should be better?

A:

I do not recommend using a KLT tracker for close CCTV cameras due to the following reasons:
1. CCTV frame rate is typically low, so people change their appearance significantly between frames
2. Since the camera is close to the people, they also change their appearance over time due to perspective effects (e.g. face can be seen when person is far from camera, but as he/she gets closer, only the top of the head is seen). 
3. Due to closeness, people also significantly change scale and aspect ratio, which is a challenge for some head detectors.
KLT only works well when the neighborhood of the pixel, including both foreground and background, remains similar. The above properties make this less likely for most pixels. I can only recommend KLT as an additional motion based hint for tracking, as a vector of field of part motions.
Most single person trackers do not adapt well to scale change. I suggest you start with some state of the art tracker, like Struck (C++ code by Sam Hare available here), and modify the search routine to work with scale change.

