Police and various security services use video analysis for securing public space, mass events, and when investigating criminal activity. Due to a huge amount of data supplied to surveillance systems, some automatic data processing is a necessity. In one typical scenario, an operator marks an object in an image frame and searches for all occurrences of the object in other frames or even image sequences. This problem is hard in general. Algorithms supporting this scenario must reconcile several seemingly contradicting factors: training and detection speed, detection reliability, and learning from small data sets. In the system proposed here, we use a two-stage detector. The first region proposal stage is based on a Cascade Classifier while the second classification stage is based either on a Support Vector Machines (SVMs) or Convolutional Neural Networks (CNNs). The proposed configuration ensures both speed and detection reliability. In addition to this, an object tracking and background-foreground separation algorithm is used, supported by the GrabCut algorithm and a sample synthesis procedure, in order to collect rich training data for the detector. Experiments show that the system is effective, useful, and applicable to practical surveillance tasks.

Robots have to perform diverse and complex tasks. To face the limitations of the computational capabilities of robot on-board control computer, it is required to split the control systems between the robot embedded and the cloud computational resources. This paper presents a reconfigurable control architecture for a robot designed to meet this requirement. The embedded computer hosts a core agent, which provides the task-independent robot capabilities. The task dependent part called the dynamic agent is loaded from the cloud when required. The two mentioned agents execute the task, additionally utilising the capabilities of the cloud. Once the task is finished the dynamic agent is destroyed and the core agent awaits new user demands, upon which it reacts by downloading a new dynamic agent. Thus a reconfigurable system results, limited only by the resources provided by the cloud. The system is presented on an example of a humanoid robot exploring a home environment in search for hazards resulting from negligence of people suffering from mild dementia.