Under a tracking framework, the definition of the target state is the basic step for automatic understanding of dynamic scenes. More specifically, far object tracking raises challenges related to the potentially abrupt size changes of the targets as they approach the sensor. If not handled, size changes can introduce heavy issues in data association and position estimation. This is why adaptability and self-awareness of a tracking module are desirable features. The paradigm of cognitive dynamic systems (CDSs) can provide a framework under which a continuously learning cognitive module can be designed. In particular, CDS theory describes a basic vocabulary of components that can be used as the founding blocks of a module capable to learn behavioral rules from continuous active interactions with the environment. This quality is the fundamental to deal with dynamic situations. In this paper we propose a general CDS-based approach to tracking. We show that such a CDS-inspired design can lead to the self-adaptability of a Bayesian tracker in fusing heterogeneous object features, overcoming size change issues. The experimental results on infrared sequences show how the proposed framework is able to outperform other existing far object tracking methods.

It is well known from physiological studies that the level of human attention for adult individuals rapidly decreases after five to twenty minutes [1]. Attention retention for a surveillance operator represents a crucial aspect in Video Surveillance applications and could have a significant impact in identifying relevance, especially in crowded situations. In this field, advanced mechanisms for selection and extraction of saliency information can improve the performances of autonomous video surveillance systems and increase the effectiveness of human operator support. In particular, crowd monitoring represents a central aspect in many practical applications for managing and preventing emergencies due to panic and overcrowding.

Detection of dim moving point targets in cluttered background can have a great impact on the tracking performances. This may become a crucial problem, especially in low-SNR environments, where target characteristics are highly susceptible to corruption. In this paper, an extended target model, namely Interacting Multiple Model (IMM), applied to Track-Before-Detect (TBD) based detection algorithm, for far objects, in infrared (IR) sequences is presented. The approach can automatically adapts the kinematic parameter estimations, such as position and velocity, in accordance with the predictions as dimensions of the target change. A sub-par sensor can cause tracking problems. In particular, for a single object, noisy observations (i.e. fragmented measures) could be associated to different tracks. In order to avoid this problem, presented framework introduces a cooperative mechanism between Joint Probabilistic Data Association Filter (JPDAF) and IMM. The experimental results on real and simulated sequences demonstrate effectiveness of the proposed approach.