Automatic extrinsic calibration of camera networks based on pedestrians

Extrinsic camera calibration is essential for any computer vision tasks in a camera network. Usually, researchers place calibration objects in the scene to calibrate the cameras. However, when installing cameras in the field, this approach can be costly and impractical, especially when recalibration is needed. This paper proposes a novel accurate and fully automatic extrinsic calibration framework for camera networks with partially overlapping views. It is based on the analysis of pedestrian tracks without other calibration objects. Compared to the state of the art, the new method is fully automatic and robust. Our method detects human poses in the camera images and then models walking persons as vertical sticks. We propose a brute-force method to determine the pedestrian correspondences in multiple camera images. This information along with 3D estimated locations of the head and feet of the pedestrians are then used to compute the camera extrinsic matrices. We verified the robustness of the method in different camera setups and for both single pedestrian and multiple walking people. The results show that the proposed method can obtain the triangulation error of a few centimeters. Typically, it requires 40 seconds of collecting data from walking people to reach this accuracy in controlled environments and a few minutes for uncontrolled environments. As well as compute relative extrinsic parameters connecting the coordinate systems of cameras in a pairwise fashion automatically. Our proposed method could perform well in various situations such as multi-person, occlusions, or even at real intersections on the street

Automatic multi-camera extrinsic parameter calibration based on pedestrian torsors

Extrinsic camera calibration is essential for any computer vision task in a camera network. Typically, researchers place a calibration object in the scene to calibrate all the cameras in a camera network. However, when installing cameras in the field, this approach can be costly and impractical, especially when recalibration is needed. This paper proposes a novel, accurate and fully automatic extrinsic calibration framework for camera networks with partially overlapping views. The proposed method considers the pedestrians in the observed scene as the calibration objects and analyzes the pedestrian tracks to obtain extrinsic parameters. Compared to the state of the art, the new method is fully automatic and robust in various environments. Our method detect human poses in the camera images and then models walking persons as vertical sticks. We apply a brute-force method to determines the correspondence between persons in multiple camera images. This information along with 3D estimated locations of the top and the bottom of the pedestrians are then used to compute the extrinsic calibration matrices. We also propose a novel method to calibrate the camera network by only using the top and centerline of the person when the bottom of the person is not available in heavily occluded scenes. We verified the robustness of the method in different camera setups and for both single and multiple walking people. The results show that the triangulation error of a few centimeters can be obtained. Typically, it requires less than one minute of observing the walking people to reach this accuracy in controlled environments. It also just takes a few minutes to collect enough data for the calibration in uncontrolled environments. Our proposed method can perform well in various situations such as multi-person, occlusions, or even at real intersections on the street

PhD forum: correlation coefficient based template matching for indoor people tracking

Abstract—One of the most popular methods to extract information from an image sequence is template matching. The principle of template matching is tracking a certain feature or target over time based on the comparison of the content of each frame with a simple template. In this article, we propose an correlation coefficient based template matching which is invariant to linear intensity distortions to do correction or verification of our existing indoor people tracking system

Extrinsic calibration of camera networks using a sphere

In this paper, we propose a novel extrinsic calibration method for camera networks using a sphere as the calibration object. First of all, we propose an easy and accurate method to estimate the 3D positions of the sphere center w.r.t. the local camera coordinate system. Then, we propose to use orthogonal procrustes analysis to pairwise estimate the initial camera relative extrinsic parameters based on the aforementioned estimation of 3D positions. Finally, an optimization routine is applied to jointly refine the extrinsic parameters for all cameras. Compared to existing sphere-based 3D position estimators which need to trace and analyse the outline of the sphere projection in the image, the proposed method requires only very simple image processing: estimating the area and the center of mass of the sphere projection. Our results demonstrate that we can get a more accurate estimate of the extrinsic parameters compared to other sphere-based methods. While existing state-of-the-art calibration methods use point like features and epipolar geometry, the proposed method uses the sphere-based 3D position estimate. This results in simpler computations and a more flexible and accurate calibration method. Experimental results show that the proposed approach is accurate, robust, flexible and easy to use

Private-Library-Oriented Code Generation with Large Language Models

Large language models (LLMs), such as Codex and GPT-4, have recently showcased their remarkable code generation abilities, facilitating a significant boost in coding efficiency. This paper will delve into utilizing LLMs for code generation in private libraries, as they are widely employed in everyday programming. Despite their remarkable capabilities, generating such private APIs poses a formidable conundrum for LLMs, as they inherently lack exposure to these private libraries during pre-training. To address this challenge, we propose a novel framework that emulates the process of programmers writing private code. This framework comprises two modules: APIFinder first retrieves potentially useful APIs from API documentation; and APICoder then leverages these retrieved APIs to generate private code. Specifically, APIFinder employs vector retrieval techniques and allows user involvement in the retrieval process. For APICoder, it can directly utilize off-the-shelf code generation models. To further cultivate explicit proficiency in invoking APIs from prompts, we continuously pre-train a reinforced version of APICoder, named CodeGenAPI. Our goal is to train the above two modules on vast public libraries, enabling generalization to private ones. Meanwhile, we create four private library benchmarks, including TorchDataEval, TorchDataComplexEval, MonkeyEval, and BeatNumEval, and meticulously handcraft test cases for each benchmark to support comprehensive evaluations. Numerous experiments on the four benchmarks consistently affirm the effectiveness of our approach. Furthermore, deeper analysis is also conducted to glean additional insights

Human mobility monitoring in very low resolution visual sensor network

This paper proposes an automated system for monitoring mobility patterns using a network of very low resolution visual sensors (30 30 pixels). The use of very low resolution sensors reduces privacy concern, cost, computation requirement and power consumption. The core of our proposed system is a robust people tracker that uses low resolution videos provided by the visual sensor network. The distributed processing architecture of our tracking system allows all image processing tasks to be done on the digital signal controller in each visual sensor. In this paper, we experimentally show that reliable tracking of people is possible using very low resolution imagery. We also compare the performance of our tracker against a state-of-the-art tracking method and show that our method outperforms. Moreover, the mobility statistics of tracks such as total distance traveled and average speed derived from trajectories are compared with those derived from ground truth given by Ultra-Wide Band sensors. The results of this comparison show that the trajectories from our system are accurate enough to obtain useful mobility statistics

Morphological and phylogenetic analyzes reveal two new species of Melanconiella from Fujian Province, China

IntroductionSpecies of Melanconiella include a diverse array of plant pathogens as well as endophytic fungi. Members of this genus have been frequently collected from the family Betulaceae (birches) in Europe and North America. Little, however, if known concerning the distribution of Melanconiella and/or their potential as pathogens of other plant hosts.MethodsFungi were noted and isolated from diseased leaves of Loropetalum chinense (Chinese fringe flower) and Camellia sinensis (tea) in Fujian Province, China. Genomic DNA was extracted from fungal isolates and the nucleotide sequences of four loci were determined and sued to construct phylogenetic trees. Morphological characteristics of fungal structures were determined via microscopic analyses.ResultsFour strains and two new species of Melanconiella were isolated from infected leaves of L. chinense and C. sinensis in Fujian Province, China. Based on morphology and a multi-gene phylogeny of the internal transcribed spacer regions with the intervening 5.8S nrRNA gene (ITS), the 28S large subunit of nuclear ribosomal RNA (LSU), the second largest subunit of RNA polymerase II (RPB2), and the translation elongation factor 1-α gene (TEF1-α), Melanconiellaloropetali sp. nov. and Melanconiellacamelliae sp. nov. were identified and described herein. Detailed descriptions, illustrations, and a key to the known species of Melanconiella are provided.DiscussionThese data identify new species of Melanconiella, expanding the potential range and distribution of these dark septate fungi. The developed keys provide a reference source for further characterization of these fungi

Assessing the structure and diversity of fungal community in plant soil under different climatic and vegetation conditions

IntroductionUnderstanding microbial communities in diverse ecosystems is crucial for unraveling the intricate relationships among microorganisms, their environment, and ecosystem processes. In this study, we investigated differences in the fungal community structure and diversity in soils from two contrasting climatic and vegetation conditions: the Xinjiang western China plateau and the Fujian southeastern coastal province.MethodsA total of 36 soil samples collected from two climatic regions were subjected to high-throughput ITS gene sequencing for fungal community analysis. In conjunction soil physicochemical properties were assessed and compared. Analyses included an examination of the relationship of fungal community structure to environmental factors and functional profiling of the community structure was using the FUNGuild pipeline.ResultsOur data revealed rich fungal diversity, with a total of 11 fungal phyla, 31 classes, 86 orders, 200 families, 388 genera, and 515 species identified in the soil samples. Distinct variations in the physicochemical properties of the soil and fungal community structure were seen in relation to climate and surface vegetation. Notably, despite a colder climate, the rhizosphere soil of Xinjiang exhibited higher fungal (α-)diversity compared to the rhizosphere soil of Fujian. β-diversity analyses indicated that soil heterogeneity and differences in fungal community structure were primarily influenced by spatial distance limitations and vegetation type. Furthermore, we identified dominant fungal phyla with significant roles in energy cycling and organic matter degradation, including members of the Sordariomycetes, Leotiomycetes, Archaeosporomycetes, and Agaricomycetes. Functional analyses of soil fungal communities highlighted distinct microbial ecological functions in Xinjiang and Fujian soils. Xinjiang soil was characterized by a focus on wood and plant saprotrophy, and endophytes, whereas in Fujian soil the fungal community was mainly associated with ectomycorrhizal interactions, fungal parasitism, and wood saprotrophy.DiscussionOur findings suggest fungal communities in different climatic conditions adapt along distinct patterns with, plants to cope with environmental stress and contribute significantly to energy metabolism and material cycling within soil-plant systems. This study provides valuable insights into the ecological diversity of fungal communities driven by geological and environmental factors

Characterization of an alpha Amylase from the honeybee chalk brood pathogen Ascosphaera apis

The insect pathogenic fungus, Ascosphaera apis, is the causative agent of honeybee chalk brood disease. Amylases are secreted by many plant pathogenic fungi to access host nutrients through the metabolism of starch, and the identification of new amylases can have important biotechnological applications. Production of amylase by A. apis in submerged culture was optimized using the response surface method (RSM). Media composition was modeled using Box–Behnken design (BBD) at three levels of three variables, and the model was experimentally validated to predict amylase activity (R2 = 0.9528). Amylase activity was highest (45.28 ± 1.16 U/mL, mean ± SE) in media composed of 46 g/L maltose and1.51 g/L CaCl2 at a pH of 6.6, where total activity was ~11-fold greater as compared to standard basal media. The enzyme was purified to homogeneity with a 2.5% yield and 14-fold purification. The purified enzyme had a molecular weight of 75 kDa and was thermostable and active in a broad pH range (> 80% activity at a pH range of 7–10), with optimal activity at 55 °C and pH = 7.5. Kinetic analyses revealed a Km of 6.22 mmol/L and a Vmax of 4.21 μmol/mL·min using soluble starch as the substrate. Activity was significantly stimulated by Fe2+ and completely inhibited by Cu2+, Mn2+, and Ba2+ (10 mM). Ethanol and chloroform (10% v/v) also caused significant levels of inhibition. The purified amylase essentially exhibited activity only on hydrolyzed soluble starch, producing mainly glucose and maltose, indicating that it is an endo-amylase (α-amylase). Amylase activity peaked at 99.38 U/mL fermented in a 3.7 L-bioreactor (2.15-fold greater than what was observed in flask cultures). These data provide a strategy for optimizing the production of enzymes from fungi and provide insight into the α-amylase of A. apis