Learning to Detect and Track Visible and Occluded Body Joints in a Virtual World

Multi-People Tracking in an open-world setting requires a special effort in precise detection. Moreover, temporal continuity in the detection phase gains more importance when scene cluttering introduces the challenging problems of occluded targets. For the purpose, we propose a deep network architecture that jointly extracts people body parts and associates them across short temporal spans. Our model explicitly deals with occluded body parts, by hallucinating plausible solutions of not visible joints. We propose a new end-to-end architecture composed by four branches (visible heatmaps, occluded heatmaps, part affinity fields and temporal affinity fields) fed by a time linker feature extractor. To overcome the lack of surveillance data with tracking, body part and occlusion annotations we created the vastest Computer Graphics dataset for people tracking in urban scenarios by exploiting a photorealistic videogame. It is up to now the vastest dataset (about 500.000 frames, almost 10 million body poses) of human body parts for people tracking in urban scenarios. Our architecture trained on virtual data exhibits good generalization capabilities also on public real tracking benchmarks, when image resolution and sharpness are high enough, producing reliable tracklets useful for further batch data association or re-id modules

Critical literacy as a pedagogical goal in English language teaching

In this chapter, the authors provide an overview of the area of critical literacy as it pertains to second language pedagogy (curriculum and instruction). After considering the historical origins of critical literacy (from antiquity, and including in first language education), they consider how it began to penetrate the field of applied linguistics. They note the geographical and institutional spread of critical literacy practice as documented by published accounts. They then sketch the main features of L2 critical literacy practice. To do this, they acknowledge how practitioners have reported on their practices regarding classroom content and process. The authors also draw attention to the outcomes of these practices as well as challenges that practitioners have encountered in incorporating critical literacy into their second language classrooms

Mentor teachers. Contributions to the development of preservice teachers’ identity

Preservice teachers’ identity development is a complex process requiring the coordination of varied internal and external factors. Internal motivations to teach, as strong as they are, might be colored by external factors influencing preservice teachers’ understanding and experiences of the teaching process. Although teacher education might have less control over the internal factors, they can significantly impact the experiences preservice teachers gain during practicum and positively shape their identity development. Mentor teachers as one of the main parties involved in teacher education exert considerable influence on preservice teachers’ understanding of who they are as a teacher and what they are capable of doing. This chapter looks at the relationship between mentor teachers and preservice teachers during practicum to unpack how mentoring and mentor teachers’ practices and mentoring approaches can motivate preservice teachers to continue as a teacher

Performance measures and a data set for multi-target, multi-camera tracking

To help accelerate progress in multi-target, multi-camera tracking systems, we present (i) a new pair of precision-recall measures of performance that treats errors of all types uniformly and emphasizes correct identification over sources of error; (ii) the largest fully-annotated and calibrated data set to date with more than 2 million frames of 1080 p, 60 fps video taken by 8 cameras observing more than 2, 700 identities over 85 min; and (iii) a reference software system as a comparison baseline. We show that (i) our measures properly account for bottom-line identity match performance in the multi-camera setting; (ii) our data set poses realistic challenges to current trackers; and (iii) the performance of our system is comparable to the state of the art