Interpolating for data sample is required in many image processing methods. For example, in the estimation of displacement which are smaller than one pixel. In the case of displacement calculation in an image sequence using the numerical method, Newton-Rapson is very common to use a linear interpolator due to its simplicity and speed. However this method generates discontinuous functions. Therefore, theoretically it should not be used in combination with Newton-Rapson due to the use of the derivative in the numerical method. This work shows a comparative analysis of different interpolators, along with a comparison between "real world" and image displacements and their relationship. All of this with the purpose of identifying which interpolator offers the most exact approximation in the estimation of displacement calculation.
Unmanned aerial vehicles have turned important in surveillance application due to the flexibility and ability to inspect and displace in different regions of interest. The instrumentation and autonomy of these vehicles have been increased; i.e. the camera sensor is now integrated. Mounted cameras allow flexibility to monitor several regions of interest, displacing and changing the camera view. A well common task performed by this kind of vehicles correspond to object localization and tracking. This work presents a hierarchical novel algorithm to detect and locate objects. The algorithm is based on a detection-by-example approach; this is, the target evidence is provided at the beginning of the vehicle's route. Afterwards, the vehicle inspects the scenario, detecting all similar objects through UTM-GPS coordinate references. Detection process consists on a sampling information process of the target object. Sampling process encode in a hierarchical tree with different sampling's densities. Coding space correspond to a huge binary space dimension. Properties such as independence and associative operators are defined in this space to construct a relation between the target object and a set of selected features. Different densities of sampling are used to discriminate from general to particular features that correspond to the target. The hierarchy is used as a way to adapt the complexity of the algorithm due to optimized battery duty cycle of the aerial device. Finally, this approach is tested in several outdoors scenarios, proving that the hierarchical algorithm works efficiently under several conditions.
This paper presents a proposal to manage simple-objects interaction in video surveillance system. The proposal consists on locating a set of features in each video frame. Maxima regions from the second Eigen- value of the tensor matrix are used as features. Afterwards, statics features are discarded (labeling as background) and dynamic features are used to represent objects in motion (foreground). Dynamics features are dynamically clustered with k-neighborhood and EM algorithm. The centroid of each cluster locally represents motion objects, and its displacement through time is denoted by displacement of cumulus over several frames. The behavior of cumulus in time help us to model simple object interactions. These primitives can be used in addition to a causal dependencies across time; i.e. cluster division, cluster fusion and cluster motion with respect to the others, offer information of local dynamics which is referred to local interactions. And based on causal dependencies theory, a graph dependence of local centroids behavior can be built. This graph can represent the local interaction model. In experimental section, the approach is tested in several scenarios, extracting simple interaction objects in controlled/not-controlled scenarios.
This article presents a strategy to model the dynamics performed by vehicles in a freeway. The proposal consists on encode the movement as a set of finite states. A watershed-based segmentation is used to localize regions with high-probability of motion. Each state represents a proportion of a camera projection in a two-dimensional space, where each state is associated to a symbol, such that any combination of symbols is expressed as a language. Starting from a sequence of symbols through a linear algorithm a free-context grammar is inferred. This grammar represents a hierarchical view of common sequences observed into the scene. Most probable grammar rules express common rules associated to normal movement behavior. Less probable rules express themselves a way to quantify non-common behaviors and they might need more attention. Finally, all sequences of symbols that does not match with the grammar rules, may express itself uncommon behaviors (abnormal). The grammar inference is built with several sequences of images taken from a freeway. Testing process uses the sequence of symbols emitted by the scenario, matching the grammar rules with common freeway behaviors. The process of detect abnormal/normal behaviors is managed as the task of verify if any word generated by the scenario is recognized by the grammar.
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