Object Tracking: A Survey [A. Yilmaz et al]

Object Tracking Method

We have point tracking method , kernel tracking methol, and silhouette tracking method.

Point Tracking Method

Deterministic : constrain the correspondence problem using qualitative motion heuristic
[Veenman et al. 2001]

• The combination of the constrin used are as follow:

• Proximity - constant background
• Maximum velocity - defines maximum displacement r and the circular area form from the obtained radius, r.
• Small velocity change
• Common motion - the multiple points that represent the object have similar movement.
• Rigidity - assume the object in 3D is rigid therefore the distance at time t-2, t-1, and t are the same. (confirm again)
• Proximal uniformity - combination of proximity and small velocity change.

• History

• 1987 Sethi nad Jain solve the correspondance using greedy approach based on the proximity and rigidity constrain. The algorithm consider two consecutive frame and is initialized by the nearest neighbor criterion. However this algorithm can'thandle occlusions, entries or exit.
• 1990 Salari and Sethi improve the algorithm by first establishing correspondence for the detectedpoints and then extending the tracking of the missing objects by adding a number of hypothetical points
• 1991 Rangarajan and Shah proposed greedy method using proximal uniformity constrain. Initial correxpondences are ontained by computing optical flow in the first two frames. The algorithm can't handle exit and entries. For occlusions, the problem is solved by establishing the correspondence for the detected object in the current frame and the position of the remaining object is predicted based on the constant velocity assupmtion.
• 1997 Intille et al.modified Rangarajan and Shah [1991] for matching object centroids. Object detected using background subtraction. Exit and entries handle explicitly by eximining the image region looking for a door to detect exit or entries
• 2001 Veenman et al. extend both Sethi and Jain [1987] and Rangarajan and Shah [1991] works by introducing the common motion constrain for correspondence.The algorith is initialized by generating the initial tracks using a two-pass algorithm, and cost function is minimized by Hungarian assignment algorithm in two consecutive frames. can handle occlusion and misdetection but not exit and entries.
• 2003 Shafique and Shah propose multiframe approach to preserve temporal coherency of the speed and position.Fin the best unique path for each point. [x clear sgt]

Statistical Method: take the object measurements and uncertainties into account to establish
correspondence.

Use space state approach to model the object properties such as position, velocity, and acceleration. Measurements obtained by detection mechanism which usually consist of object position in the image

Single Object State Estimation

• Kalman Filter: used to estimate the stae of linear system and have Gaussian distribution. Compose of prediction and correction steps.

• Prediction step uses the state model to predict the new state of the variables
• Correction step uses the current obzervation to update the object's state
• Matlab Toolbox: KalmanSrc

• Particle Filter: used for non Gaussian distribution [Tanizaki 1987]

• The conditional state density at time t is represented by a set of sampling particles with weight (sampling probability).
• The weight define the importance of a sample (observation frequency) [Isard and Blake 1998]
• The common sampling scheme is importance sampling

• Selection: Select N random Sample
• Prediction: Generate new sample with zero mean Gaussian Error and non-negative function
• Correction: Weight corresponding to the new sample are computed using the measurement equation which can be modeled as a Gaussian density

• Particle filter -based tracker is initialized by either using the first measurements or by training the system using sample sequences.
• maltab toolbox available at ParticleFlSrc.

Multiobject Data Association and State Estimation

Requires a joiny solution of data association and state estimation problem.
Before applying Kalman or Particle Filter one must deterministicallyassociate the most likely measurement for a particular object to that object's state.

• Joint Probability Data Association Filter (JPDAF)
• Multiple Hypothesis Tracking (MHT)

Note: will continue once have better undestanding