Methodology for an integrated modelling of macro and microscopic processes in urban transport demand

The paper presents the theoretical formulation and the underlying assumptions for an activity-based approach of transport demand modelling. Starting with the definition of a time hierarchy of decision-making in the urban environment, rules are formulated that dictate the general hierarchic structure of individuals’ choices in the urban system. The temporal scale defines decisions for activities and their daily sequence, the geographical scale decisions associated to destination choice processes. We build activity plans (number and daily sequence of activities) from an empirical data set and calculate trip paths (time-spatial trajectories including transport modes and travel destinations) assuming consumers to maximize their utility in the decision-making process. First results of the translation of the theoretical model into a real-world application are shown for the city of Santiago, Chile

Social Scene Understanding: End-to-End Multi-Person Action Localization and Collective Activity Recognition

We present a unified framework for understanding human social behaviors in raw image sequences. Our model jointly detects multiple individuals, infers their social actions, and estimates the collective actions with a single feed-forward pass through a neural network. We propose a single architecture that does not rely on external detection algorithms but rather is trained end-to-end to generate dense proposal maps that are refined via a novel inference scheme. The temporal consistency is handled via a person-level matching Recurrent Neural Network. The complete model takes as input a sequence of frames and outputs detections along with the estimates of individual actions and collective activities. We demonstrate state-of-the-art performance of our algorithm on multiple publicly available benchmarks

The Understanding of Human Activities by Computer Vision Techniques

Esta tesis propone nuevas metodologías para el aprendizaje de actividades humanas y su clasificación en categorías. Aunque este tema ha sido ampliamente estudiado por la comunidad investigadora en visión por computador, aún encontramos importantes dificultades por resolver. En primer lugar hemos encontrado que la literatura sobre técnicas de visión por computador para el aprendizaje de actividades humanas empleando pocas secuencias de entrenamiento es escasa y además presenta resultados pobres [1] [2]. Sin embargo, este aprendizaje es una herramienta crucial en varios escenarios. Por ejemplo, un sistema de reconocimiento recién desplegado necesita mucho tiempo para adquirir nuevas secuencias de entrenamiento así que el entrenamiento con pocos ejemplos puede acelerar la puesta en funcionamiento. También la detección de comportamientos anómalos, ejemplos de los cuales son difíciles de obtener, puede beneficiarse de estas técnicas. Existen soluciones mediante técnicas de cruce dominios o empleando características invariantes, sin embargo estas soluciones omiten información del escenario objetivo la cual reduce el ruido en el sistema mejorando los resultados cuando se tiene en cuenta y ejemplos de actividades anómalas siguen siendo difíciles de obtener. Estos sistemas entrenados con poca información se enfrentan a dos problemas principales: por una parte el sistema de entrenamiento puede sufrir de inestabilidades numéricas en la estimación de los parámetros del modelo, por otra, existe una falta de información representativa proveniente de actividades diversas. Nos hemos enfrentado a estos problemas proponiendo novedosos métodos para el aprendizaje de actividades humanas usando tan solo un ejemplo, lo que se denomina one-shot learning. Nuestras propuestas se basan en sistemas generativos, derivadas de los Modelos Ocultos de Markov[3][4], puesto que cada clase de actividad debe ser aprendida con tan solo un ejemplo. Además, hemos ampliado la diversidad de información en los modelos aplicado una transferencia de información desde fuentes externas al escenario[5]. En esta tesis se explican varias propuestas y se muestra como con ellas hemos conseguidos resultados en el estado del arte en tres bases de datos públicas [6][7][8]. La segunda dificultad a la que nos hemos enfrentado es el reconocimiento de actividades sin restricciones en el escenario. En este caso no tiene por qué coincidir el escenario de entrenamiento y el de evaluación por lo que la reducción de ruido anteriormente expuesta no es aplicable. Esto supone que se pueda emplear cualquier ejemplo etiquetado para entrenamiento independientemente del escenario de origen. Esta libertad nos permite extraer vídeos desde cualquier fuente evitando la restricción en el número de ejemplos de entrenamiento. Teniendo suficientes ejemplos de entrenamiento tanto métodos generativos como discriminativos pueden ser empleados. En el momento de realización de esta tesis encontramos que el estado del arte obtiene los mejores resultados empleando métodos discriminativos, sin embargo, la mayoría de propuestas no suelen considerar la información temporal a largo plazo de las actividades[9]. Esta información puede ser crucial para distinguir entre actividades donde el orden de sub-acciones es determinante, y puede ser una ayuda en otras situaciones[10]. Para ello hemos diseñado un sistema que incluye dicha información en una Máquina de Vectores de Soporte. Además, el sistema permite cierta flexibilidad en la alineación de las secuencias a comparar, característica muy útil si la segmentación de las actividades no es perfecta. Utilizando este sistema hemos obtenido resultados en el estado del arte para cuatro bases de datos complejas sin restricciones en los escenarios[11][12][13][14]. Los trabajos realizados en esta tesis han servido para realizar tres artículos en revistas del primer cuartil [15][16][17], dos ya publicados y otro enviado. Además, se han publicado 8 artículos en congresos internacionales y uno nacional [18][19][20][21][22][23][24][25][26]. [1]Seo, H. J. and Milanfar, P. (2011). Action recognition from one example. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(5):867–882.(2011) [2]Yang, Y., Saleemi, I., and Shah, M. Discovering motion primitives for unsupervised grouping and one-shot learning of human actions, gestures, and expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(7):1635–1648. (2013) [3]Rabiner, L. R. A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257–286. (1989) [4]Bishop, C. M. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, Inc., Secaucus, NJ, USA. (2006) [5]Cook, D., Feuz, K., and Krishnan, N. Transfer learning for activity recognition: a survey. Knowledge and Information Systems, pages 1–20. (2013) [6]Schuldt, C., Laptev, I., and Caputo, B. Recognizing human actions: a local svm approach. In International Conference on Pattern Recognition (ICPR). (2004) [7]Weinland, D., Ronfard, R., and Boyer, E. Free viewpoint action recognition using motion history volumes. Computer Vision and Image Understanding, 104(2-3):249–257. (2006) [8]Gorelick, L., Blank, M., Shechtman, E., Irani, M., and Basri, R. Actions as space-time shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(12):2247–2253. (2007) [9]Wang, H. and Schmid, C. Action recognition with improved trajectories. In IEEE International Conference on Computer Vision (ICCV). (2013) [10]Choi, J., Wang, Z., Lee, S.-C., and Jeon, W. J. A spatio-temporal pyramid matching for video retrieval. Computer Vision and Image Understanding, 117(6):660 – 669. (2013) [11]Oh, S., Hoogs, A., Perera, A., Cuntoor, N., Chen, C.-C., Lee, J. T., Mukherjee, S., Aggarwal, J. K., Lee, H., Davis, L., Swears, E., Wang, X., Ji, Q., Reddy, K., Shah, M., Vondrick, C., Pirsiavash, H., Ramanan, D., Yuen, J., Torralba, A., Song, B., Fong, A., Roy-Chowdhury, A., and Desai, M. A large-scale benchmark dataset for event recognition in surveillance video. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3153–3160. (2011) [12] Niebles, J. C., Chen, C.-W., and Fei-Fei, L. Modeling temporal structure of decomposable motion segments for activity classification. In European Conference on Computer Vision (ECCV), pages 392–405.(2010) [13]Reddy, K. K. and Shah, M. Recognizing 50 human action categories of web videos. Machine Vision and Applications, 24(5):971–981. (2013) [14]Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., and Serre, T. HMDB: a large video database for human motion recognition. In IEEE International Conference on Computer Vision (ICCV). (2011) [15]Rodriguez, M., Orrite, C., Medrano, C., and Makris, D. One-shot learning of human activity with an map adapted gmm and simplex-hmm. IEEE Transactions on Cybernetics, PP(99):1–12. (2016) [16]Rodriguez, M., Orrite, C., Medrano, C., and Makris, D. A time flexible kernel framework for video-based activity recognition. Image and Vision Computing 48-49:26 – 36. (2016) [17]Rodriguez, M., Orrite, C., Medrano, C., and Makris, D. Extended Study for One-shot Learning of Human Activity by a Simplex-HMM. IEEE Transactions on Cybernetics (Enviado) [18]Orrite, C., Rodriguez, M., Medrano, C. One-shot learning of temporal sequences using a distance dependent Chinese Restaurant Process. In Proceedings of the 23nd International Conference Pattern Recognition ICPR (December 2016) [19]Rodriguez, M., Medrano, C., Herrero, E., and Orrite, C. Spectral Clustering Using Friendship Path Similarity Proceedings of the 7th Iberian Conference, IbPRIA (June 2015) [20]Orrite, C., Soler, J., Rodriguez, M., Herrero, E., and Casas, R. Image-based location recognition and scenario modelling. In Proceedings of the 10th International Conference on Computer Vision Theory and Applications, VISAPP (March 2015) [21]Castán, D., Rodríguez, M., Ortega, A., Orrite, C., and Lleida, E. Vivolab and cvlab - mediaeval 2014: Violent scenes detection affect task. In Working Notes Proceedings of the MediaEval (October 2014) [22]Orrite, C., Rodriguez, M., Herrero, E., Rogez, G., and Velastin, S. A. Automatic segmentation and recognition of human actions in monocular sequences In Proceedings of the 22nd International Conference Pattern Recognition ICPR (August 2014) [23]Rodriguez, M., Medrano, C., Herrero, E., and Orrite, C. Transfer learning of human poses for action recognition. In 4th International Workshop of Human Behavior Unterstanding (HBU). (October 2013) [24]Rodriguez, M., Orrite, C., and Medrano, C. Human action recognition with limited labelled data. In Actas del III Workshop de Reconocimiento de Formas y Analisis de Imagenes, WSRFAI. (September 2013) [25]Orrite, C., Monforte, P., Rodriguez, M., and Herrero, E. Human Action Recognition under Partial Occlusions . Proceedings of the 6th Iberian Conference, IbPRIA (June 2013) [26]Orrite, C., Rodriguez, M., and Montañes, M. One sequence learning of human actions. In 2nd International Workshop of Human Behavior Unterstanding (HBU). (November 2011)This thesis provides some novel frameworks for learning human activities and for further classifying them into categories. This field of research has been largely studied by the computer vision community however there are still many drawbacks to solve. First, we have found few proposals in the literature for learning human activities from limited number of sequences. However, this learning is critical in several scenarios. For instance, in the initial stage after a system installation the capture of activity examples is time expensive and therefore, the learning with limited examples may accelerate the operational launch of the system. Moreover, examples for training abnormal behaviour are hardly obtainable and their learning may benefit from the same techniques. This problem is solved by some approaches, such as cross domain implementations or the use of invariant features, but they do not consider the specific scenario information which is useful for reducing the clutter and improving the results. Systems trained with scarce information face two main problems: on the one hand, the training process may suffer from numerical instabilities while estimating the model parameters; on the other hand, the model lacks of representative information coming from a diverse set of activity classes. We have dealt with these problems providing some novel approaches for learning human activities from one example, what is called a one-shot learning method. To do so, we have proposed generative approaches based on Hidden Markov Models as we need to learn each activity class from only one example. In addition, we have transferred information from external sources in order to introduce diverse information into the model. This thesis explains our proposals and shows how these methods achieve state-of-the-art results in three public datasets. Second, we have studied the recognition of human activities in unconstrained scenarios. In this case, the scenario may or may not be repeated in training and evaluation and therefore the clutter reduction previously mentioned does not happen. On the other hand, we can use any labelled video for training the system independently of the target scenario. This freedom allows the extraction of videos from the Internet dismissing the implicit constrains when training with limited examples. Having plenty of training examples both, generative and discriminative, methods can be used and by the time this thesis has been made the state-of-the-art has been achieved by discriminative ones. However, most of the methods usually fail when taking into consideration long-term information of the activities. This information is critical when comparing activities where the order of sub-actions is important, and may be useful in other comparisons as well. Thus, we have designed a framework that incorporates this information in a discriminative classifier. In addition, this method introduces some flexibility for sequence alignment, useful feature when the activity segmentation is not exact. Using this framework we have obtained state-of-the-art results in four challenging public datasets with unconstrained scenarios

Integrated Land Use-Transport Model System with Dynamic Time-Dependent Activity-Travel Microsimulation

The development of integrated land use-transport model systems has long been of interest because of the complex interrelationships between land use, transport demand, and network supply. This paper describes the design and prototype implementation of an integrated model system that involves the microsimulation of location choices in the land use domain, activity-travel choices in the travel demand domain, and individual vehicles on networks in the network supply modeling domain. Although many previous applications of integrated transport demand-supply models have relied on a sequential coupling of the models, the system presented in this paper involves a dynamic integration of the activity-travel demand model and the dynamic traffic assignment and simulation model with appropriate feedback to the land use model system. The system has been fully implemented, and initial results of model system runs in a case study test application suggest that the proposed model design provides a robust behavioral framework for simulation of human activity-travel behavior in space, time, and networks. The paper provides a detailed description of the design, together with results from initial test runs

Going Deeper into Action Recognition: A Survey

Understanding human actions in visual data is tied to advances in complementary research areas including object recognition, human dynamics, domain adaptation and semantic segmentation. Over the last decade, human action analysis evolved from earlier schemes that are often limited to controlled environments to nowadays advanced solutions that can learn from millions of videos and apply to almost all daily activities. Given the broad range of applications from video surveillance to human-computer interaction, scientific milestones in action recognition are achieved more rapidly, eventually leading to the demise of what used to be good in a short time. This motivated us to provide a comprehensive review of the notable steps taken towards recognizing human actions. To this end, we start our discussion with the pioneering methods that use handcrafted representations, and then, navigate into the realm of deep learning based approaches. We aim to remain objective throughout this survey, touching upon encouraging improvements as well as inevitable fallbacks, in the hope of raising fresh questions and motivating new research directions for the reader

Recognition of Activities of Daily Living with Egocentric Vision: A Review.

Video-based recognition of activities of daily living (ADLs) is being used in ambient assisted living systems in order to support the independent living of older people. However, current systems based on cameras located in the environment present a number of problems, such as occlusions and a limited field of view. Recently, wearable cameras have begun to be exploited. This paper presents a review of the state of the art of egocentric vision systems for the recognition of ADLs following a hierarchical structure: motion, action and activity levels, where each level provides higher semantic information and involves a longer time frame. The current egocentric vision literature suggests that ADLs recognition is mainly driven by the objects present in the scene, especially those associated with specific tasks. However, although object-based approaches have proven popular, object recognition remains a challenge due to the intra-class variations found in unconstrained scenarios. As a consequence, the performance of current systems is far from satisfactory

Recognising high-level agent behaviour through observations in data scarce domains

This thesis presents a novel method for performing multi-agent behaviour recognition without requiring large training corpora. The reduced need for data means that robust probabilistic recognition can be performed within domains where annotated datasets are traditionally unavailable (e.g. surveillance, defence). Human behaviours are composed from sequences of underlying activities that can be used as salient features. We do not assume that the exact temporal ordering of such features is necessary, so can represent behaviours using an unordered “bag-of-features”. A weak temporal ordering is imposed during inference to match behaviours to observations and replaces the learnt model parameters used by competing methods. Our three-tier architecture comprises low-level video tracking, event analysis and high-level inference. High-level inference is performed using a new, cascading extension of the Rao-Blackwellised Particle Filter. Behaviours are recognised at multiple levels of abstraction and can contain a mixture of solo and multiagent behaviour. We validate our framework using the PETS 2006 video surveillance dataset and our own video sequences, in addition to a large corpus of simulated data. We achieve a mean recognition precision of 96.4% on the simulated data and 89.3% on the combined video data. Our “bag-of-features” framework is able to detect when behaviours terminate and accurately explains agent behaviour despite significant quantities of low-level classification errors in the input, and can even detect agents who change their behaviour