Emilio Maggio

Hybrid particle filter and mean shift tracker with adaptive transition model

We propose a tracking algorithm based on a combination of particle filter and mean shift, and enhanced with a new adaptive state transition model. The particle filter is robust to partial and total occlusions, can deal with multi-modal pdf and can recover lost tracks. However, its complexity dramatically increases with the dimensionality of the sampled pdf. Mean shift has a low complexity, but is unable to deal with multi-modal pdf. To overcome these problems, the proposed tracker first produces a smaller number of samples than the particle filter and then shifts the samples toward a close local maximum using mean shift. The transition model predicts the state based on adaptive variances. Experimental results show that the combined tracker outperforms the particle filter and mean shift in terms of accuracy in estimating the target size and position while generating 80% less samples than the particle filter.

Adaptive Multifeature Tracking in a Particle Filtering Framework

In this paper, we propose a tracking algorithm based on an adaptive multifeature statistical target model. The features are combined in a single particle filter by weighting their contributions using a novel reliability measure derived from the particle distribution in the state space. This measure estimates the reliability of the information by measuring the spatial uncertainty of features. A modified resampling strategy is also devised to account for the needs of the feature reliability estimation. We demonstrate the algorithm using color and orientation features. Color is described with partwise normalized histograms. Orientation is described with histograms of the gradient directions that represent the shape and the internal edges of a target. A feedback from the state estimation is used to align the orientation histograms as well as to adapt the scales of the filters to compute the gradient. Experimental results over a set of real-world sequences show that the proposed feature weighting procedure outperforms state-of-the-art solutions and that the proposed adaptive multifeature tracker improves the reliability of the target estimate while eliminating the need of manually selecting each features relevance.

Learning Scene Context for Multiple Object Tracking

We propose a framework for multitarget tracking with feedback that accounts for scene contextual information. We demonstrate the framework on two types of context-dependent events, namely target births (i.e., objects entering the scene or reappearing after occlusion) and spatially persistent clutter. The spatial distributions of birth and clutter events are incrementally learned based on mixtures of Gaussians. The corresponding models are used by a probability hypothesis density (PHD) filter that spatially modulates its strength based on the learned contextual information. Experimental results on a large video surveillance dataset using a standard evaluation protocol show that the feedback improves the tracking accuracy from 9% to 14% by reducing the number of false detections and false trajectories. This performance improvement is achieved without increasing the computational complexity of the tracker.

Multi-feature graph-based object tracking

We present an object detection and tracking algorithm that addresses the problem of multiple simultaneous targets tracking in real-world surveillance scenarios. The algorithm is based on color change detection and multi-feature graph matching. The change detector uses statistical information from each color channel to discriminate between foreground and background. Changes of global illumination, dark scenes, and cast shadows are dealt with a pre-processing and post-processing stage. Graph theory is used to find the best object paths across multiple frames using a set of weighted object features, namely color, position, direction and size. The effectiveness of the proposed algorithm and the improvements in accuracy and precision introduced by the use of multiple features are evaluated on the VACE dataset.

Accurate appearance-based Bayesian tracking for maneuvering targets

We propose a tracking algorithm that combines the Mean Shift search in a Particle Filtering framework and a target representation that uses multiple semi-overlapping color histograms. The target representation introduces spatial information that accounts for rotation and anisotropic scaling without compromising the flexibility typical of color histograms. Moreover, the proposed tracker can generate a smaller number of samples than Particle Filter as it increases the particle efficiency by moving the samples toward close local maxima of the likelihood using Mean Shift. Experimental results show that the proposed representation improves the robustness to clutter and that, especially on highly maneuvering targets, the combined tracker outperforms Particle Filter and Mean Shift in terms of accuracy in estimating the target size and position while generating only 25% of the samples used by Particle Filter.

Combining Colour and Orientation for Adaptive Particle Filter-based Tracking.

We propose an accurate tracking algorithm based on a multi-feature statistical model. The model combines in a single particle filter colour and gradient-based orientation information. A reliability measure derived from the particle distribution is used to adaptively weigh the contribution of the two features. Furthermore, information from the tracker is used to set the dimension of the filters for the computation of the gradient, effectively solving the scale selection problem. Experiments over a set of real-world sequences show that the adaptive use of colour and orientation information improves over either feature taken separately, both in terms of tracking accuracy and of reduction of lost tracks. Also, the automatic scale selection for the derivative filters results in increased robustness.

Objective Evaluation of Pedestrian and Vehicle Tracking on the CLEAR Surveillance Dataset

Video object detection and tracking in surveillance scenarios is a difficult task due to several challenges caused by environmental variations, scene dynamics and noise introduced by the CCTV camera itself. In this paper, we analyse the performance of an object detector and tracker based on background subtraction followed by a graph matching procedure for data association. The analysis is performed based on the CLEAR dataset. In particular, we discuss a set of solutions to improve the robustness of the detector in case of various types of natural light changes, sensor noise, missed detection and merged objects. The proposed solutions and various parameter settings are analysed and compared based on 1 hour 21 minutes of CCTV surveillance footage and its associated ground truth and the CLEAR evaluation metrics.

Hybrid video coding based on bidimensional matching pursuit

Hybrid video coding combines together two stages: first, motion estimation and compensation predict each frame from the neighboring frames, then the prediction error is coded, reducing the correlation in the spatial domain. In this work, we focus on the latter stage, presenting a scheme that profits from some of the features introduced by the standard H.264/AVC for motion estimation and replaces the transform in the spatial domain. The prediction error is so coded using the matching pursuit algorithm which decomposes the signal over an appositely designed bidimensional, anisotropic, redundant dictionary. Comparisons are made among the proposed technique, H.264, and a DCT-based coding scheme. Moreover, we introduce fast techniques for atom selection, which exploit the spatial localization of the atoms. An adaptive coding scheme aimed at optimizing the resource allocation is also presented, together with a rate-distortion study for the matching pursuit algorithm. Results show that the proposed scheme outperforms the standard DCT, especially at very low bit rates.

Grouping motion trajectories

We present a method to group trajectories of moving objects extracted from real-world surveillance videos. The trajectories are first mapped into a low dimensionality feature space generated through linear regression. Next the regression coefficients are clustered by a Gaussian Mixture Model initialized by K-means for improved efficiency. The model selection problem is solved with Bayesian Information Criterion that penalizes models with high complexity. We demonstrate the proposed approach on both synthetic and real-world scenes. Experimental results show that the proposed clustering method outperforms K-means and mixture of regression models, while also reducing the computational complexity compared to the latter.

Tracking Atoms with Particles for Audio-Visual Source Localization

We present a general framework and an efficient algorithm for tracking relevant video structures. The structures to be tracked are implicitly defined by a matching pursuit procedure that extracts and ranks the most important image contours. Based on the ranking, the contours are automatically selected to initialize a particle filtering tracker. The proposed algorithm deals with salient video entities whose behavior has an intuitive meaning, related to the physics of the signal. Moreover, as the interactions between such structures are easily defined, the inference of higher level signal configurations can be made intuitive. The proposed algorithm improves the performance of existing video structures trackers, while reducing the computational complexity. The algorithm is demonstrated on audio-visual source localization.