Evaluation of probabilistic occupancy map people detection for surveillance systems

In this paper, we evaluate the Probabilistic Occupancy Map (POM) pedestrian detection algorithm on the PETS 2009 benchmark dataset. POM is a multi-camera generative detection method, which estimates ground plane occupancy from multiple background subtraction views. Occupancy probabilities are iteratively estimated by fitting a synthetic model of the background subtraction to the binary foreground motion. Furthermore, we test the integration of this algorithm into a larger framework designed for understanding human activities in real environments. We demonstrate accurate detection and localization on the PETS dataset, despite suboptimal calibration and foreground motion segmentation input.

PETS2009 and Winter-PETS 2009 results: a combined evaluation

This paper presents the results of the crowd image analysis challenge of the Winter PETS 2009 workshop. The evaluation is carried out using a selection of the metrics developed in the Video Analysis and Content Extraction (VACE) program and the CLassification of Events, Activities, and Relationships (CLEAR) consortium [13]. The evaluation highlights the detection and tracking performance of the authors’systems in areas such as precision, accuracy and robustness. The performance is also compared to the PETS 2009 submitted results.

PETS2010 and PETS2009 evaluation of results using individual ground truthed single views

This paper presents the results of the crowd image analysis challenge of the PETS2010 workshop. The evaluation was carried out using a selection of the metrics developed in the Video Analysis and Content Extraction (VACE) program and the CLassification of Events, Activities, and Relationships (CLEAR) consortium. The PETS 2010 evaluation was performed using new ground truthing create from each independant two dimensional view. In addition, the performance of the submissions to the PETS 2009 and Winter-PETS 2009 were evaluated and included in the results. The evaluation highlights the detection and tracking performance of the authors’ systems in areas such as precision, accuracy and robustness.

Dimetallic complexes of macrocycles with two rigid dibenzofuran units as receptors for detection of anionic substrates

The hexaazamacrocycles [28](DBF)2N6 {cyclo[bis(4,6-dimethyldibenzo[b,d]furaniminoethyleneiminoethylene]} and [32](DBF)2N6 {cyclo[bis(4,6-dimethyldibenzo[b,d]furaniminopropyleneiminopropylene]} form stable dinuclear copper(II) complexes suitable to behave as receptors for several anionic substrates. These two receptors were used to study the binding interactions with several substrates, such as imidazole (Him) and some carboxylates [benzoate (bz−), oxalate (ox2−), malonate (mal2−), phthalate (ph2−), isophthalate (iph2−), and terephthalate (tph2−)] by spectrophotometric titrations and EPR spectroscopy in MeOH (or H2O):DMSO (1:1 v/v) solution. The largest association constant was found for ox2− with Cu2[32](DBF)2N64+, whereas for the aromatic dicarboxylate anions the binding constants follow the trend ph2− > iph2− > tph2−, i.e. decrease with the increase of the distance of the two binding sites of the substrate. On the other hand, the large blue shift of 68 nm observed by addition of Him to Cu2[32](DBF)2N64+ points out for the formation of the bridged CuimCu cascade complex, indicating this receptor as a potential sensor for the detection and determination of imidazole in solution. The X-band EPR spectra of the Cu2[28](DBF)2N64+ and Cu2[32](DBF)2N6]4+ complexes and the cascade complexes with the substrates, performed in H2O:DMSO (1:1 v/v) at 5 to 15 K, showed that the CuCu distance is slightly larger than the one found in crystal state and that this distance increases when the substrate is accommodated between the two copper centres. The crystal structure of [Cu2[28](DBF)2N6(ph)2]·CH3OH was determined by X-ray diffraction and revealed the two copper centres bridged by two ph2− anions at a Cu···Cu distance of 5.419(1) Å. Each copper centre is surrounded by three carboxylate oxygen atoms from two phthalate anions and three contiguous nitrogen atoms of the macrocycle in a pseudo octahedral coordination environment.

Combined bias and outlier identification in dynamic data reconciliation

Measured process data normally contain inaccuracies because the measurements are obtained using imperfect instruments. As well as random errors one can expect systematic bias caused by miscalibrated instruments or outliers caused by process peaks such as sudden power fluctuations. Data reconciliation is the adjustment of a set of process data based on a model of the process so that the derived estimates conform to natural laws. In this paper, techniques for the detection and identification of both systematic bias and outliers in dynamic process data are presented. A novel technique for the detection and identification of systematic bias is formulated and presented. The problem of detection, identification and elimination of outliers is also treated using a modified version of a previously available clustering technique. These techniques are also combined to provide a global dynamic data reconciliation (DDR) strategy. The algorithms presented are tested in isolation and in combination using dynamic simulations of two continuous stirred tank reactors (CSTR).