Streamlining collection of training samples for object detection and classification in video

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Generative modeling of spatio-temporal traffic sign trajectories ∗

We consider the task of automatic detection and recognition of traffic signs in video. We show that successful offthe-shelf detection (Viola-Jones) and classification (SVM) systems yield unsatisfactory results. Our main concern are high false positive detection rates which occur due to sparseness of the traffic signs in videos. We address the problem by enforcing spatio-temporal consistency of the detections corresponding to a distinct sign in video. We also propose a generative model of the traffic sign motion in the image plane, which is obtained by clustering the trajectories filtered by an appropriate procedure. The contextual information recovered by the proposed model will be employed in our future research on recognizing traffic signs in video. 1

Visualizing and quantifying movement from pre-recorded videos: The spectral time-lapse (STL) algorithm

When studying animal behaviour within an open environment, movement-related data are often important for behavioural analyses. Therefore, simple and efficient techniques are needed to present and analyze the data of such movements. However, it is challenging to present both spatial and temporal information of movements within a two-dimensional image representation. To address this challenge, we developed the spectral time-lapse (STL) algorithm that re-codes an animal’s position at every time point with a time-specific color, and overlays it with a reference frame of the video, to produce a summary image. We additionally incorporated automated motion tracking, such that the animal’s position can be extracted and summary statistics such as path length and duration can be calculated, as well as instantaneous velocity and acceleration. Here we describe the STL algorithm and offer a freely available MATLAB toolbox that implements the algorithm and allows for a large degree of end-user control and flexibility

Algal Communities Along the Sava River

Field analysis of phytoplankton and phytobenthos communities of the river Sava has been performed, from Slovenia to Serbia, in August 2011 and September 2012 at 20 localities. A total number of 256 taxa have been determined, from eight divisions: Cyanobacteria (20), Rhodophyta (1), Dinophyta (6), Cryptophyta (1), Chrysophyta (1), Bacillariophyta (152), Chlorophyta (67) and Euglenophyta (8). In the phytoplankton samples, 188 taxa have been identified and in the phytobenthos samples 153 taxa. The most diverse divisions of phytoplankton of the river Sava were Bacillariophyta (46.28 % of total taxa number) and Chlorophyta (34.57 % of total taxa number). Biomass of phytoplankton was low, and the abundance of phytoplankton communities varied between 65,000 and 412,000 Ind L−1. The biomass of phytoplankton of the river Sava was in the range of 41 to 564 μg fr. wt. L−1. The phytobenthos dominated by the division of Bacillariophyta, making 81.7 % of the community. Visible macroaggregations were composed of Cladophora glomerata (Chlorophyta) and Thorea hispida (Rhodophyta).Milačić R, Ščančar J, Paunović M, editors. The Sava River. Berlin, Heidelberg: Springer-Verlag; 2015. p. 229-48