Human detection from aerial imagery for automatic counting of shellfish gatherers

International audienceAutomatic human identification from aerial image time series or video sequences is a challenging issue. We propose here a complete processing chain that operates in the context of recreational shellfish gatherers counting in a coastal environment (the Gulf of Morbihan, South Brittany, France). It starts from a series of aerial photographs and builds a mosaic in order to prevent multiple occurrences of the same objects on the overlapping parts of aerial images. To do so, several stitching techniques are reviewed and discussed in the context of large aerial scenes. Then people detection is addressed through a sliding window analysis combining the HOG descriptor and a supervised classifier. Several classification methods are compared, including SVM, Random Forests, and AdaBoost. Experimental results show the interest of the proposed approach, and provides directions for future research

A Scheme for the Detection and Tracking of People Tuned for Aerial Image Sequences

Abstract. This paper addresses the problem of detecting and tracking a large number of individuals in aerial image sequences that have been taken from high altitude. We propose a method which can handle the numerous challenges that are associated with this task and demonstrate its quality on several test sequences. Moreover this paper contains several contributions to improve object detection and tracking in other domains, too. We show how to build an effective object detector in a flexible way which incorporates the shadow of an object and enhanced features for shape and color. Furthermore the performance of the detector is boosted by an improved way to collect background samples for the classifier train-ing. At last we describe a tracking-by-detection method that can handle frequent misses and a very large number of similar objects

Requirements and Limitations of Thermal Drones for Effective Search and Rescue in Marine and Coastal Areas

Search and rescue (SAR) is a vital line of defense against unnecessary loss of life. However, in a potentially hazardous environment, it is important to balance the risks associated with SAR action. Drones have the potential to help with the efficiency, success rate and safety of SAR operations as they can cover large or hard to access areas quickly. The addition of thermal cameras to the drones provides the potential for automated and reliable detection of people in need of rescue. We performed a pilot study with a thermal-equipped drone for SAR applications in Morecambe Bay. In a variety of realistic SAR scenarios, we found that we could detect humans who would be in need of rescue, both by the naked eye and by a simple automated method. We explore the current advantages and limitations of thermal drone systems, and outline the future path to a useful system for deployment in real-life SAR

Design and performance assessment of correlation filters for the detection of objects in high clutter thermal imagery

The research reported in this thesis has examined means of enhancing the performance of the Optimal Trade-off Maximum Average Correlation Height (OT-MACH) filter for target detection in Forward Looking Infra-Red (FLIR) imagery acquired from a helicopter and border security FLIR camera in northern Kuwait. The data acquired with these FLIR sensors allows real-world evaluation of the comparative performance of the various filters that have been developed in the thesis. The results obtained have been quantified using well known performance measures such as Peak to Side-lobe Ratio (PSR) and Total Detection Error (TDE). The initial focus was to study the effect of modifying the OT-MACH parameters on the correlation metrics. A new optimisation technique has been presented, which computes statistically the filter alpha parameter associated with controlling the response of the filter to clutter noise. A further modification of the OT-MACH filter performance using the Difference of Gaussian bandpass filter (named the D-MACH filter) as a pre-processing stage has been described. The D-MACH has been applied to several test images containing single and multiple targets in the scene. Enhanced performance of the modified filter is demonstrated with improved metrics being obtained with less false side peaks in the correlation plane, especially when multiple targets are present in the test images. A further pre-processing technique was investigated using the Rayleigh distribution as a pre-processing filter (named the R-MACH filter). The R-MACH filter has been applied to multiple target types with tests conducted across various image data sets. The filter demonstrated an improvement over the Difference of Gaussian filter in terms of 6 reducing the number of parameters needing to be tuned whilst producing further enhanced correlation plane metrics. Finally, recommendations for future work has been made to improve the use of the OT-MACH filter in target detection and identification. A novel training image representation is proposed for further investigation, which will minimise the computational intensity of using the MACH filter for unconstrained object recognition

Vision-based traffic monitoring system with hierarchical camera auto-calibration

Texto en inglés.En las últimas décadas, el tráfico, debido al aumento de su volumen y al consiguiente incremento en la demanda de infraestructuras de transporte, se ha convertido en un gran problema en ciudades de casi todo el mundo. Constituye un fenómeno social, económico y medioambiental en el que se encuentra inmersa toda la sociedad, por lo que resulta importante tomarlo como un aspecto clave a mejorar. En esta línea, y para garantizar una movilidad segura, fluida y sostenible, es importante analizar el comportamiento e interacción de los vehículos y peatones en diferentes escenarios. Hasta el momento, esta tarea se ha llevado a cabo de forma limitada por operarios en los centros de control de tráfico. Sin embargo, el avance de la tecnología, sugiere una evolución en la metodología hacia sistemas automáticos de monitorización y control. Este trabajo se inscribe en el marco de los Sistemas Inteligentes de Transporte (ITS), concretamente en el ámbito de la monitorización para la detección y predicción de incidencias (accidentes, maniobras peligrosas, colapsos, etc.) en zonas críticas de infraestructuras de tráfico, como rotondas o intersecciones. Para ello se propone el enfoque de la visión artificial, con el objetivo de diseñar un sistema sensor compuesto de una cámara, capaz de medir de forma robusta parámetros correspondientes a peatones y vehículos que proporcionen información a un futuro sistema de detección de incidencias, control de tráfico, etc.El problema general de la visión artificial en este tipo de aplicaciones, y que es donde se hace hincapié en la solución propuesta, es la adaptabilidad del algoritmo a cualquier condición externa. De esta forma, cambios en la iluminación o en la meteorología, inestabilidades debido a viento o vibraciones, oclusiones, etc. son compensadas. Además el funcionamiento es independiente de la posición de la cámara, con la posibilidad de utilizar modelos con pan-tilt-zoom variable para aumentar la versatilidad del sistema. Una de las aportaciones de esta tesis es la extracción y uso de puntos de fuga (a partir de elementos estructurados de la escena), para obtener una calibración de la cámara sin conocimiento previo. Esta calibración proporciona un tamaño aproximado de los objetos buscados, mejorando así el rendimiento de las siguientes etapas del algoritmo. Para segmentar la imagen se realiza una extracción de los objetos móviles a partir del modelado del fondo, basándose en mezcla de Gaussianas (GMM) y métodos de detección de sombras. En cuanto al seguimiento de los objetos segmentados, se desecha la idea tradicional de considerarlos un conjunto. Para ello se extraen características cuya evolución es analizada para conseguir finalmente una agrupación óptima que sea capaz de solventar oclusiones. El sistema ha sido probado en condiciones de tráfico real sin ningún conocimiento previo de la escena, con resultados bastante satisfactorios que muestran la viabilidad del método

Geometric Constraints for Human Detection in Aerial Imagery

Abstract. In this paper, we propose a method for detecting humans in imagery taken from a UAV. This is a challenging problem due to small number of pixels on target, which makes it more difficult to distinguish people from background clutter, and results in much larger searchspace. We propose a method for human detection based on a number of geometric constraints obtained from the metadata. Specifically, we obtain the orientation of groundplane normal, the orientation of shadows cast by humans in the scene, and the relationship between human heights and the size of their corresponding shadows. In cases when metadata is not available we propose a method for automatically estimating shadow orientation from image data. We utilize the above information in a geometry based shadow, and human blob detector, which provides an initial estimation for locations of humans in the scene. These candidate locations are then classified as either human or clutter using a combination of wavelet features, and a Support Vector Machine. Our method works on a single frame, and unlike motion detection based methods, it bypasses the global motion compensation process, and allows for detection of stationary and slow moving humans, while avoiding the search across the entire image, which makes it more accurate and very fast. We show impressive results on sequences from the VIVID dataset and our own data, and provide comparative analysis.

Geometric Constraints For Human Detection In Aerial Imagery

In this paper, we propose a method for detecting humans in imagery taken from a UAV. This is a challenging problem due to small number of pixels on target, which makes it more difficult to distinguish people from background clutter, and results in much larger searchspace. We propose a method for human detection based on a number of geometric constraints obtained from the metadata. Specifically, we obtain the orientation of groundplane normal, the orientation of shadows cast by humans in the scene, and the relationship between human heights and the size of their corresponding shadows. In cases when metadata is not available we propose a method for automatically estimating shadow orientation from image data. We utilize the above information in a geometry based shadow, and human blob detector, which provides an initial estimation for locations of humans in the scene. These candidate locations are then classified as either human or clutter using a combination of wavelet features, and a Support Vector Machine. Our method works on a single frame, and unlike motion detection based methods, it bypasses the global motion compensation process, and allows for detection of stationary and slow moving humans, while avoiding the search across the entire image, which makes it more accurate and very fast. We show impressive results on sequences from the VIVID dataset and our own data, and provide comparative analysis. © 2010 Springer-Verlag