Generalization of Costly Pain-Related Avoidance Based on Real-Life Categorical Knowledge.

Avoiding activities posing bodily threat is adaptive. However, spreading of avoidance to safe activities may cause functional disability in people with chronic pain. We investigated whether costly pain-related avoidance would generalize from one activity to another on the basis of real-life categorical knowledge in 40 pain-free people (30 female; mean age = 25 years; university students and public of Maastricht, The Netherlands). In a computer task, participants moved a joystick to complete activities from two categories (gardening and cleaning). During activities from the avoidance category, pain could be avoided at the cost of task efficiency by deviating from a short, pain-associated joystick movement. Activities from the safe category were never painful. Subsequently, we tested generalization of avoidance to novel pain-free activities from both categories. Participants generalized avoidance to novel activities from the avoidance category despite the novel activities not being paired with pain and despite avoidance costs, suggesting that costly (pain-related) avoidance generalizes from one activity to another on the basis of category knowledge and can thus be wide reaching, creating detrimental consequences.

Multiple Object Tracking for Dense Pedestrians by Markov Random Field Model with Improvement on Potentials.

Pedestrian tracking in dense crowds is a challenging task, even when using a multi-camera system. In this paper, a new Markov random field (MRF) model is proposed for the association of tracklet couplings. Equipped with a new potential function improvement method, this model can associate the small tracklet coupling segments caused by dense pedestrian crowds. The tracklet couplings in this paper are obtained through a data fusion method based on image mutual information. This method calculates the spatial relationships of tracklet pairs by integrating position and motion information, and adopts the human key point detection method for correction of the position data of incomplete and deviated detections in dense crowds. The MRF potential function improvement method for dense pedestrian scenes includes assimilation and extension processing, as well as a message selective belief propagation algorithm. The former enhances the information of the fragmented tracklets by means of a soft link with longer tracklets and expands through sharing to improve the potentials of the adjacent nodes, whereas the latter uses a message selection rule to prevent unreliable messages of fragmented tracklet couplings from being spread throughout the MRF network. With the help of the iterative belief propagation algorithm, the potentials of the model are improved to achieve valid association of the tracklet coupling fragments, such that dense pedestrians can be tracked more robustly. Modular experiments and system-level experiments are conducted using the PETS2009 experimental data set, where the experimental results reveal that the proposed method has superior tracking performance.