people (re)identification

PERSON REIDENTIFICATION

Reidentification methods can be roughly divided into single-shot and multiple-shot approaches. The former have only one occurrence of the individual to be searched, while the latter integrate information over time using multiple views of the subject by her/his tracking in the video-stream where s/he is indicated as suspect by an operator (or by an intelligent module of a surveillance platform). In general, the features to describe the suspect can be biometric (gait, height) and/or appearance-based (clothes, pieces of clothes, case). The selection depends on the resolution of the images and the field of view of the cameras. In any case, selected features are used to build a signature of the person. Then the same kind of features are extracted from the frames of streams captured by the surveillance cameras, possibly in restricted regions, e.g. only where people move, and are compared with the signature of the suspect, therefore detecting possible locations of her/his presence.

Re-identification can provide a useful tool to re-identify and track impaired or elderly people that pass from a room to another in a surveilled house. In multi-camera surveillance systems, this technology is used to capture the presence of suspicious persons in complex environments such as train stations, assisting security operators by providing hypotheses of re-identifications. Tracking with re-identification provides useful information for improving shopping space management.

TeV contributed to the development of a video-surveillance platform with re-identification modules designed to assist security operators, therefore providing hypotheses of re-identifications. We proposed BFiVe, a supervised algorithm for single-shot person re-identification. How it works:

image description

• Image of the person is split into a set of rectangular receptive fields (differently sized, overlapped);

• The following 5 color channels are considered: H, S, L, Cb, Cr, and handled in a separate way;

• Image pixels I(x,y) are described by vectors of low level features: I, x, y, Gx, Gy, Gxx, Gyy for each channel;

• For each receptive field, a Fisher vector description is built for each channel. They are concatenated and compressed (PCA) to obtain a single descriptor of the receptive field;

• The image is described by the set of local descriptors.

weak scoring functions

• For each receptive field we collect the set of descriptors computed on the images in the training set (containing pairs of images depicting the same person).

• From this set a scoring function is learnt:

  • differences among descriptors are computed and split into two sets: S (difference of vectors depicting the same person) and D (otherwise);

  • the scoring function is an estimate of the log-likelihood ratio log P(x|S) / P(x|D) under the hypothesis that S and D have multivariate Gaussian distribution.

boosting

• A boosting procedure is iteratively applied to select the weak scoring producing a ranking with minimum error:

the error is the given by the number of samples (image pairs) not correctly ranked, i.e. pair depicting the same person is not at rank 1, or pairs depicting different individuals have rank lower than the correct one.

• Final scoring function is the linear combination of the selected weak scoring function with a weight selected as well by the boosting procedure.

random partitions

Comparison of people re-identification algorithms require tests on common available databases. In supervised algorithms, the database is randomly split in two disjoint parts, the former is used for the training phase, the latter for the test. Since results are affected by the splitting of the databases, it is common practice to repeat the whole process using different random partitions averaging the results to represent the algorithm performance. We provide all the random partitions of four standard re-identification databases (namely: VIPeR, 3DPeS, PRID2011, iLIDS119) that we have used to train and test BFiVe. Making our partition available, the improvement of the state-of-the-art with respect to BFiVe can be measured on exactly the same runs, avoiding the random splitting factor.

publications

S. Messelodi and C.M. Modena. Boosting Fisher Vector based Scoring Functions for Person Re-Identification, Image and Vision Computing, Vol. 44, pp. 44-58, 2015

N. Conci, F.G.B. De Natale, S. Messelodi, C.M. Modena, M. Verza, and R. Fioravanti. An integrated framework for video surveillance in complex environments. IEEE International Smart Cities Conference - ISC2, 2016