This paper presents a novel method to count people for video surveillance applications. Methods in the literature either follow a direct approach, by first detecting people and then counting them, or an indirect approach, by establishing a relation between some easily detectable scene features and the estimated number of people. The indirect approach is considerably more robust, but it is not easy to take into account such factors as perspective or people groups with different densities. The proposed technique, while based on the indirect approach, specifically addresses these problems; furthermore it is based on a trainable estimator that does not require an explicit formulation of a priori knowledge about the perspective and density effects present in the scene at hand. In the experimental evaluation, the method has been extensively compared with the algorithm by Albiol et al., which provided the highest performance at the PETS 2009 contest on people counting. The experimentation has used the public PETS 2009 datasets. The results confirm that the proposed method improves the accuracy, while retaining the robustness of the indirect approach
People counting is an important problem in video surveillance applications. This problem has been faced either by trying to detect people in the scene and then counting them or by establishing a mapping between some scene feature and the number of people (avoiding the complex detection problem). This paper presents a novel method, following this second approach, that is based on the use of SURF features and of an-SVR regressor provide an estimate of this count. The algorithm takes specifically into account problems due to partial occlusions and to perspective. In the experimental evaluation, the proposed method has been compared with the algorithm by Albiol et al., winner of the PETS 2009 contest on people counting, using the same PETS 2009 database. The provided results confirm that the proposed method yields an improved accuracy, while retaining the robustness of Albiol's algorithm.
Advanced Driver-Assistance Systems (ADASs) are currently gaining particular attention in the automotive field, as enablers for vehicle energy consumption, safety, and comfort enhancement. Compelling evidence is in fact provided by the variety of related studies that are to be found in the literature. Moreover, considering the actual technology readiness, larger opportunities might stem from the combination of ADASs and vehicle connectivity. Nevertheless, the definition of a suitable control system is not often trivial, especially when dealing with multiple-objective problems and dynamics complexity. In this scenario, even though diverse strategies are possible (e.g., Equivalent Consumption Minimization Strategy, Rule-based strategy, etc.), the Model Predictive Control (MPC) turned out to be among the most effective ones in fulfilling the aforementioned tasks. Hence, the proposed study is meant to produce a comprehensive review of MPCs applied to scenarios where ADASs are exploited and aims at providing the guidelines to select the appropriate strategy. More precisely, particular attention is paid to the prediction phase, the objective function formulation and the constraints. Subsequently, the interest is shifted to the combination of ADASs and vehicle connectivity to assess for how such information is handled by the MPC. The main results from the literature are presented and discussed, along with the integration of MPC in the optimal management of higher level connection and automation. Current gaps and challenges are addressed to, so as to possibly provide hints on future developments.
Optimization of agricultural practices is key for facing the challenges of modern agri-food systems, which are expected to satisfy a growing demand of food production in a landscape characterized by a reduction in cultivable lands and an increasing awareness of sustainability issues. In this work, an operational methodology for characterization of vegetation biomass and nitrogen content based on close-range hyperspectral remote sensing is introduced. It is based on an unsupervised active learning technique suitable for the calibration of a partial least square regression. The proposed technique relies on an innovative usage of Shannon’s entropy and allows for the set-up of an incremental monitoring framework from scratch aiming at minimizing field sampling activities. Experimental results concerning the estimation of grassland biomass and nitrogen content returned RMSE values of 2.05 t/ha and 4.68 kg/ha, respectively. They are comparable with the literature, mostly relying on supervised frameworks and confirmed the suitability of the proposed methodology with operational environments.
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