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This paper describes a new model of camera networks that can be used for environment monitoring and understanding. Such networks can be composed of both smart cameras, which benefit from high resolutions, powerful processing capabilities and strategic viewpoints on the environment, or with what we may call silly cameras, defined by much lower speci...
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... and exchanges signals with its physical neighbors. We can thus make the distinction between external events that are directly generated by some activity in the environment (e.g., detection of a target), and internal events that are generated and propagated through the network as a response to external events. All connections are illustrated on Fig. 1, and we can further refine the different input events received by each Ant-Cam as ...
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Citations
... • Re-Configuration: Self-reconfiguration behavior, in its turn, is more about learning its software parameters, and handle the calibration. The camera are modelled, figuratively, by ants [BKMQB16], auctioneer [ELYR14], gene [IBMC09] and even gamer [MZA + 13]. While all of the representation aims to mitigate the problem of dependency, each one is challenging with different aspect. ...
... For this, we need to consider the set of observed/memorized paths in the camera network, with associated delays. We evaluate this term using the transition matrix [BKMQB16] with the adjusted probabilities having the temporal similarity φ t and the spatial information φ s . ...
... BKMQB16] and[MBKQ + 16]. In addition, it is considered impossible to cover up all the possible case upstream. ...
The Ant-Cams network is a new model of camera networks that can be used for environment monitoring and understanding. Usually, such networks are composed of smart cameras, which benefit from high resolutions, powerful processing capabilities and strategic viewpoints on the environment. Here, the network uses silly cameras, defined by much lower specifications forming the Ant-Cam model. This latter is inspired from the world of ants, where ants are able to solve complex problems by communicating despite their limited capabilities.This model can reach efficient high-level understanding in spite of the limited information provided by each silly camera. We rather focus on the interactions between those cameras to increase the performance of the system where data exchanged between the cameras, such as timing or features characterizing the events, is as important as the visual information extracted locally.Unlike many existing visual sensor network which require some prior knowledge of the network such as position and neighbors, the Ant-Cams do not require any knowledge about the network configuration (e.g. camera location). Once starting working and the system reaches a steady state, all the necessary information can be found through interacting with neighbors. Thus, we can find the topology of the network where links are reinforced based on observed transitions, the paths adopted by the targets and if space covering is sufficient.
... Notice that the architecture shown in Figure 2 presents the WiseNET system as a centralize system where there is no communication between the smart cameras, however the system could also be deployed in a distributed manner as presented in [2]. ...
... SPARQL is a recursive acronym for SPARQL Protocol and RDF Query Language.2 Currently (February, 2017) SWRL is not a W3C recommendation yet. ...
The contextual information in the built environment is highly heterogeneous, it goes from static information (e.g.,
information about the building structure) to dynamic information (e.g., user’s space-time information, sensors detections and
events that occurred). This paper proposes to semantically fuse the building’s contextual information with extracted data from
a smart camera network by using ontologies and semantic web technologies. The developed ontology allows interoperability
between the different contextual data and enables, without human interaction, real-time event detections and system reconfigura-
tion to be performed. The use of semantic knowledge in multi-camera monitoring systems guarantees the protection of the user’s
privacy by not sending nor saving any image, just extracting the knowledge from them. This paper presents a new approach to
develop an "all-seeing" smart building, where the global system is the first step to attempt to provide Artificial Intelligence (AI)
to a building.
Nous proposons un système dedétection et de suivi des personnes enmouvement dans des grands espaces. Notresolution repose sur un réseau de camérasintelligentes pour l'extraction desinformations spatio-temporelles despersonnes. Les caméras sont composées d'uncapteur 3D, d'un système embarqué et decommunication. Nous avons montrél'efficacité du placement des capteurs 3D enposition zénithale par rapport auxoccultations et variations d’échelle.Nous garantissons l'exécution des traitementsen temps-réel (~20 fps), permettantde détecter des déplacements rapides avecune précision jusqu’à 99 %, et capable d’unfiltrage paramétrique des cibles non désiréescomme les enfants ou les caddies.Nous avons réalisé une étude sur la viabilitétechnologique des résultats pour de grandsespaces, rendant la solution industrialisable
Most of the existing platforms of smart camera networks rely on high-specification cameras with a high resolution visual sensor and high performed processors to provide high efficiency. However, for ethics and privacy concern, or to reduce computation requirement and power consumption, we can opt for low resolution. In this paper, we present a new platform LobNet for low-specification cameras Ant-Cam using a mouse sensor providing 30*30 pixels. An FPGA is used for processing which allows having completely distributed computing. Also, SmartMesh IP is utilized for communication. Low resolution can not provide much information and the silhouette observed by cameras may completely change depending on the coming point. However, focusing on this transformation between each pair of cameras can provide re-identification information.
Over the last years, the rapid growth of distributed smart cameras has triggered the search for new approaches of smartness of cameras to have better results. As communication among camera entities is becoming more and more complex and new ways of modeling communication have been proposed. These new ways have been taking inspiration from different fields such as socio-economic approach or game theory. Moreover, one of the major problems of the camera network is re-identification. However, in most cases, the interaction between cameras presupposes that the latter are able to perform perfect and unambiguous detections, thus limiting the decision tasks to the Markovian model. Within this paper, we present a new approach of interaction between cameras based on a non-Markovian model. To resolve this issue, we can exploit other types of information rather than visual information to improve re-identification. This information is Spatial, Visual and Temporal (SVT). Temporal information holds the time needed to go from one camera to another, while spatial information contains the path followed by the target which is a key point for the decision-making process. This offers the possibility for the network to learn regularities and then reach a steady state.
This demonstrator aims to present a network model of silly cameras defined by their low specifications. To handle those specifications, a strong collaboration is established between the cameras, inspired from the ants world. Thus we introduce the Ant-Cam. The demonstrator uses a color sensor, light processing and wireless communications. Scattered in the environment without any prior knowledge about the environment, the cameras are able to learn regularities then the network reaches a stable state.
