Damian Campo

Online pedestrian group walking event detection using spectral analysis of motion similarity graph

A method for online identification of group of moving objects in the video is proposed in this paper. This method at each frame identifies group of tracked objects with similar local instantaneous motion pattern using spectral clustering on motion similarity graph. Then, the output of the algorithm is used to detect the event of more than two object moving together as required by PETS2015 challenge. The performance of the algorithm is evaluated on the PETS2015 dataset.

Static force field representation of environments based on agents’ nonlinear motions

This paper presents a methodology that aims at the incremental representation of areas inside environments in terms of attractive forces. It is proposed a parametric representation of velocity fields ruling the dynamics of moving agents. It is assumed that attractive spots in the environment are responsible for modifying the motion of agents. A switching model is used to describe near and far velocity fields, which in turn are used to learn attractive characteristics of environments. The effect of such areas is considered radial over all the scene. Based on the estimation of attractive areas, a map that describes their effects in terms of their localizations, ranges of action, and intensities is derived in an online way. Information of static attractive areas is added dynamically into a set of filters that describes possible interactions between moving agents and an environment. The proposed approach is first evaluated on synthetic data; posteriorly, the method is applied on real trajectories coming from moving pedestrians in an indoor environment.

Active estimation of motivational spots for modeling dynamic interactions

To understand the behavior of moving entities in a given environment, one should be capable of predicting their motion, that is, to model their dynamics. In a setting where different behaviors can arise, one can assume that each of them corresponds to different motivational states of observed entities. Here, those motivations are understood as goal positions or spots where entities seek to arrive. To build prediction models based on that idea, we present an unsupervised method to estimate motivational spots actively. Additionally, we use the output of such process to refine an adaptive system modeling the dynamics of inferred hidden causes of observed data. The whole method uses deep variational methods, and particularly, the network estimating motivations is trained through dynamic programming. Results show that modeling the dynamics of entities can be better achieved by integrating information about motivational spots. Notably, a network modeling the dynamics converges faster through the incorporation of information about motivations.

Jammer detection algorithm for wide-band radios using spectral correlation and neural networks

Cognitive radio (CR) is a promising technology for future wireless spectrum allocation to improve the use of licensed bands. However, security challenges faced by cognitive radio technology are still a hot research topic. One of prevailing challenges is the radio frequency jamming attack, where adversaries are able to exploit on-the-fly reconfigurability potentials and learning mechanism of cognitive radios in order to devise and deploy advanced jamming tactics. Jamming attacks can significantly impact the performance of wireless communication systems and lead to significant overheads in terms of retransmission and increment of power consumption. In this context, a novel jammer detection algorithm is proposed using cyclic spectral analysis and artificial neural networks (ANN) for wide-band (WB) cognitive radios. The proposed approach assumes a WB spectrum occupied by various narrow-band (NB) signals, which can be either legitimate or jamming signals. The second order statistics, namely, the spectral correlation function (SCF) and ANN are used to classify each NB signal as a legitimate or jamming signal. The algorithm performance is shown with the help of simulations.

Modeling and classification of trajectories based on a Gaussian process decomposition into discrete components

We present a method to model and classify trajectory data that come from surveillance videos. Observations of the locations of moving entities are used to estimate their expected velocity in the scene. Such estimation is performed by a Gaussian process regression that enables to approximate probabilistically the expected velocity of entities given some observed evidence in the scene. Subsequently, regions where estimations have high certainty are decomposed into zones by superpixel segmentation. Each zone represents a region where motions of entities can be explained by a quasilinear dynamical model. We evaluated the proposed method with two datasets and confirmed its reliability for characterizing and classifying trajectories.