We present a method for estimating activities of multiple, interacting objects detected by a video surveillance system. The activities are described in a stochastic context because our method is concerned with humans and uses noisy features detected from video. To monitor activities in this context, we introduce the concept of an attribute set for each blob, consisting of object type, action, and interaction. Using probabilistic relations introduced by a specific Markov model of these attribute sets, the activity descriptions are estimated from surveillance video.

Human faces convey various information, including that is specific to each individual person and that is part of mutual communication among persons. Information exhibited by a "face" is what is called "non-verbal information" and usually verbal media cannot easily describe such information appropriately. Recently, detailed studies on the processing of face images by a computer have been carried out in the engineering field for applications to communication media and human computer interaction as well as automatic identification of human faces. Two main technical topics are the recognition of human faces and the synthesis of face images. The objective of the former is to enable a computer to detect and identify users and further to recognize their facial expressions, while that of the latter is to provide a natural and impressive user interface on a computer in the form of a "face. " These studies have also been found to be useful in various non-engineering fields related to a face, such as psychology, anthropology, cosmetology and dentistry. Most of the studies in these different fields have been carried out independently up to now, although all of them deal with a "face. " Now in virtue of the progress in the above engineering technologies a common study tools and databases for facial information have become available. On the basis of these backgrounds, this paper surveys recent research trends in the processing of face images by a computer and its typical applications. Firstly, the various characteristics of faces are considered. Secondly, recent research activities in the recognition and synthesis of face images are outlined. Thirdly, the applications of digital processing methods of facial information are discussed from several standpoints: intelligent image coding, media handling, human computer interaction, caricature, facial impression, psychological and medical applications. The common tools and databases used in the studies of processing of facial information and some related topics are also described.

Robot path-planning is one of the important issues in robotic navigation. This paper presents a novel robot path-planning approach based on the associative memory using Self-Organizing Incremental Neural Networks (SOINN). By the proposed method, an environment is first autonomously divided into a set of path-fragments by junctions. Each fragment is represented by a sequence of preliminarily generated common patterns (CPs). In an online manner, a robot regards the current path as the associative path-fragments, each connected by junctions. The reasoning technique is additionally proposed for decision making at each junction to speed up the exploration time. Distinct from other methods, our method does not ignore the important information about the regions between junctions (path-fragments). The resultant number of path-fragments is also less than other method. Evaluation is done via Webots physical 3D-simulated and real robot experiments, where only distance sensors are available. Results show that our method can represent the environment effectively; it enables the robot to solve the goal-oriented navigation problem in only one episode, which is actually less than that necessary for most of the Reinforcement Learning (RL) based methods. The running time is proved finite and scales well with the environment. The resultant number of path-fragments matches well to the environment.

A novel Position-Invariant Robust Feature, designated as PIRF, is presented to address the problem of highly dynamic scene recognition. The PIRF is obtained by identifying existing local features (i.e. SIFT) that have a wide baseline visibility within a place (one place contains more than one sequential images). These wide-baseline visible features are then represented as a single PIRF, which is computed as an average of all descriptors associated with the PIRF. Particularly, PIRFs are robust against highly dynamical changes in scene: a single PIRF can be matched correctly against many features from many dynamical images. This paper also describes an approach to using these features for scene recognition. Recognition proceeds by matching an individual PIRF to a set of features from test images, with subsequent majority voting to identify a place with the highest matched PIRF. The PIRF system is trained and tested on 2000+ outdoor omnidirectional images and on COLD datasets. Despite its simplicity, PIRF offers a markedly better rate of recognition for dynamic outdoor scenes (ca. 90%) than the use of other features. Additionally, a robot navigation system based on PIRF (PIRF-Nav) can outperform other incremental topological mapping methods in terms of time (70% less) and memory. The number of PIRFs can be reduced further to reduce the time while retaining high accuracy, which makes it suitable for long-term recognition and localization.

This paper presents a new incremental approach for robot navigation using associative memory. We defined the association as node→action→node where node is the robot position and action is the action of a robot (i.e., orientation, direction). These associations are used for path planning by retrieving a sequence of path fragments (in form of (node→action→node) → (node→action→node) →…) starting from the start point to the goal. To learn such associations, we applied the associative memory using Self-Organizing Incremental Associative Memory (SOIAM). Our proposed method comprises three layers: input layer, memory layer and associative layer. The input layer is used for collecting input observations. The memory layer clusters the obtained observations into a set of topological nodes incrementally. In the associative layer, the associative memory is used as the topological map where nodes are associated with actions. The advantages of our method are that 1) it does not need prior knowledge, 2) it can process data in continuous space which is very important for real-world robot navigation and 3) it can learn in an incremental unsupervised manner. Experiments are done with a realistic robot simulator: Webots. We divided the experiments into 4 parts to show the ability of creating a map, incremental learning and symbol-based recognition. Results show that our method offers a 90% success rate for reaching the goal.