The costs of fusion in smart camera networks

ABSTRACT The choice of the most suitable fusion scheme for smart camera networks depends on the application as well as on the available computational and communication resources. In this paper we discuss and compare the resource requirements of five fusion schemes, namely centralised fusion, flooding, consensus, token passing and dynamic clustering. The Extended Information Filter is applied to each fusion scheme to perform target tracking. Token passing and dynamic clustering involve negotiation among viewing nodes (cameras observing the same target) to decide which node should perform the fusion process whereas flooding and consensus do not include this negotiation. Negotiation helps limiting the number of participating cameras and reduces the required resources for the fusion process itself but requires additional communication. Consensus has the highest communication and computation costs but it is the only scheme that can be applied when not all viewing nodes are connected directly and routing tables are not available

Description of the exposure of the most-followed spanish instamom's children to social medias

There is evidence of the risk of overexposure of children on social networks by parents working as influencers. A cross-sectional study of the profiles of the sixteen most-followed Instamoms in Spain was carried out. An analysis of these profiles was performed over a full month (April 2022), three times a week, to describe the representation of influencers’ children in the posts shared by them, as well as their role in the Instamoms’ marketing. A total of 192 evaluations of the profiles were performed in the study period. The average number of children exposed by an Instamom was three, generally preschoolers and schoolchildren. The children appear in a context of the family home and accompanied by their mother. The type of advertising that accompanies the appearance of underage children is usually women or children’s clothing, but also food products, leisure, etc. Appearance of children in the posts had a statistically significant influence on followers measured by the number of likes. Results provided the identification of two Instamom clusters with differentiated behaviors in relation to appearance of children in posts. It is important to involve Social Pediatrics in the protection of the privacy and interests of children given the increase in sharenting. The authors believe that there are concerns about their explicit consent to public exposure from early childhood and about the medium and long-term effect that this may have on their future well-being

Phylogenetic history demonstrates two different lineages of dengue type 1 virus in Colombia

Background: Dengue Fever is one of the most important viral re-emergent diseases affecting about 50 million people around the world especially in tropical and sub-tropical countries. In Colombia, the virus was first detected in the earliest 70′s when the disease became a major public health concern. Since then, all four serotypes of the virus have been reported. Although most of the huge outbreaks reported in this country have involved dengue virus serotype 1 (DENV-1), there are not studies about its origin, genetic diversity and distribution. Results: We used 224 bp corresponding to the carboxyl terminus of envelope (E) gene from 74 Colombian isolates in order to reconstruct phylogenetic relationships and to estimate time divergences. Analyzed DENV-1 Colombian isolates belonged to the formerly defined genotype V. Only one virus isolate was clasified in the genotype I, likely representing a sole introduction that did not spread. The oldest strains were closely related to those detected for the first time in America in 1977 from the Caribbean and were detected for two years until their disappearance about six years later. Around 1987, a split up generated 2 lineages that have been evolving separately, although not major aminoacid changes in the analyzed region were found. Conclusion: DENV-1 has been circulating since 1978 in Colombia. Yet, the phylogenetic relationships between strains isolated along the covered period of time suggests that viral strains detected in some years, although belonging to the same genotype V, have different recent origins corresponding to multiple re-introduction events of viral strains that were circulating in neighbor countries. Viral strains used in the present study did not form a monophyletic group, which is evidence of a polyphyletic origin. We report the rapid spread patterns and high evolution rate of the different DENV-1 lineages

Detection-aware multi-object tracking evaluation

How would you fairly evaluate two multi-object tracking algorithms (i.e. trackers), each one employing a different object detector? Detectors keep improving, thus trackers can make less effort to estimate object states over time. Is it then fair to compare a new tracker employing a new detector with another tracker using an old detector? In this paper, we propose a novel performance measure, named Tracking Effort Measure (TEM), to evaluate trackers that use different detectors. TEM estimates the improvement that the tracker does with respect to its input data (i.e. detections) at frame level (intra-frame complexity) and sequence level (inter-frame complexity). We evaluate TEM over well-known datasets, four trackers and eight detection sets. Results show that, unlike conventional tracking evaluation measures, TEM can quantify the effort done by the tracker with a reduced correlation on the input detections. Its implementation will be made publicly available online. 1 . 1 https://github.com/vpulab/MOT-evaluatio

Coarse-to-Fine Adaptive People Detection for Video Sequences by Maximizing Mutual Information ^†

Applying people detectors to unseen data is challenging since patterns distributions, such as viewpoints, motion, poses, backgrounds, occlusions and people sizes, may significantly differ from the ones of the training dataset. In this paper, we propose a coarse-to-fine framework to adapt frame by frame people detectors during runtime classification, without requiring any additional manually labeled ground truth apart from the offline training of the detection model. Such adaptation make use of multiple detectors mutual information, i.e., similarities and dissimilarities of detectors estimated and agreed by pair-wise correlating their outputs. Globally, the proposed adaptation discriminates between relevant instants in a video sequence, i.e., identifies the representative frames for an adaptation of the system. Locally, the proposed adaptation identifies the best configuration (i.e., detection threshold) of each detector under analysis, maximizing the mutual information to obtain the detection threshold of each detector. The proposed coarse-to-fine approach does not require training the detectors for each new scenario and uses standard people detector outputs, i.e., bounding boxes. The experimental results demonstrate that the proposed approach outperforms state-of-the-art detectors whose optimal threshold configurations are previously determined and fixed from offline training data

Graph Neural Networks for Cross-Camera Data Association

Cross-camera image data association is essential for many multi-camera computer vision tasks, such as multi-camera pedestrian detection, multi-camera multi-target tracking, 3D pose estimation, etc. This association task is typically stated as a bipartite graph matching problem and often solved by applying minimum-cost flow techniques, which may be computationally inefficient with large data. Furthermore, cameras are usually treated by pairs, obtaining local solutions, rather than finding a global solution at once. Other key issue is that of the affinity measurement: the widespread usage of non-learnable pre-defined distances, such as the Euclidean and Cosine ones. This paper proposes an efficient approach for cross-cameras data-association focused on a global solution, instead of processing cameras by pairs. To avoid the usage of fixed distances, we leverage the connectivity of Graph Neural Networks, previously unused in this scope, using a Message Passing Network to jointly learn features and similarity. We validate the proposal for pedestrian multi-view association, showing results over the EPFL multi-camera pedestrian dataset. Our approach considerably outperforms the literature data association techniques, without requiring to be trained in the same scenario in which it is tested. Our code is available at http://www-vpu.eps.uam.es/publications/gnn_cca