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1 Approximated tracking of multiple non-rigid objects using adaptive quantization and resampling techniques. J. M. Sotoca 1, F.J. Ferri 1, J. Gutierrez 1, P. Remagnino 2, G.A. Jones 2. 1 Dept. d’Informática. Universitat de València. Spain 2 School of Computing and Information Systems. Kingston University. United Kingdom.

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2 Objectives: Obtain an approach to track events in indoor scenes with partial occlusion. The conditional density propagation (CONDENSATION) algorithm is used to propagate events. The LVQ algorithm is used to effectively maintain alternative predictions for each object.

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3 Tracking Moving Objects Tracking: composite process of observing, predicting and interpreting. Several different possible contexts: clutter, several objects, different behaviours,... Severe restrictions or reduce domains have to be considered.

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4 Tracking as a Stochastic Estimation problem Events or objects to track are modelled by a pdf over a state space (x i, p i ). At discrete times, the measurements vector y i becomes available. We will follow the two steps of prediction and correction. In the prediction, the state variables change over time according to dynamic equation. In the correction, we need to take the prediction, which acts as a prior, and turn it into a posterior. For this, we can apply the Bayes theorem. The problem can be then stated as propagating hypotheses about state variables given previous and actual information about both hypotheses and measurements.

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5 Tracking as a Stochastic Estimation problem

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6 The CONDENSATION algorithm Characteristics of the CONDENSATION algorithm: Is based on factored sampling but extended to apply iteratively to successive images in a sequence. Propagation is done by a weighted resampling technique according to a measure of likelihood. Utilise a cumulative probabilities to select that elements survive.

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7 The CONDENSATION algorithm

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8 Interpretation of scenes with some objects In the condensation context, the only difference relies on the interpretation of the obtained pdf for each frame. The existence of occlusion in the trajectory of the object makes difficult to propagate good solutions. Events appearing and disappearing must be explicitly taken into account.

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9 Background Model Background subtraction technique to obtain the activity zones. Partial occlusion generate several blobs for each object. Existence of noise in the scene.

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10 Interpreting the parameter space. Study in the parameter space using LVQ algorithm to see how many groups exit in the sample. Choose that groups belong an objects and that one are noise. Correlation between interpretations of successive frames treated as a correspondence problem.

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11 Experimental Results Study of indoor scene where different objects (on the table) hide the persons that moves across the scene. Reduced set of parameters per object (12- dimensional state space). The persons can change to direction during the surveillance process. Low level of noise. No significant clutter.

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12 Experimental Results State space

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13 Experimental Results Frame 25

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14 Experimental Results Frame 41

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15 Experimental Results Frame 47

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16 Conclusions and Future Lines Condensation can be adapted in several ways to track multiple objects at the same time. Initialisation, new object detection require special non trivial treatment. The proposed scheme constitutes a first step but can be enhanced in several ways. Use of measures more complex how the optical flow in the region corresponding to the object.

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