This article aims to enhance understanding of the changing nature of the pre-colonial, (neo)colonial and postcolonial imagination of space and time in Africa and of its organising principle in African cinema. It will focus on the cartographic and time reckoning techniques and traditions of Africans in precolonial times in contrast to the space-time imagination expressed in colonial film in Africa, such as in the instruction documentary Daybreak in Udi (1949). This documentary, which promotes British colonial self-help development projects in Africa, (...) tells the story of the building of a maternity home in an Igbo village in Nigeria. Furthermore, the article will enhance understanding of the cinematic turn to a counterhegemonic film tradition as a stepping stone to the newly emerging field of African intercultural philosophical cinema. The time-space imagination in Common Threads (2018), shortlisted by the Zanzibar International Film Festival (ZIFF) for the best documentary will be analysed as a case in point. This documentary concentrates on the nineteenth-century and current Afro-Indian textile trade, the associated oral narratives and their visual impact on the so-called Kanga and Vitenge textiles. (shrink)

Traffic sign detection and recognition (TSDR) is a critical aspect of autonomous driving and intelligent transportation systems. Traditional methods of traffic sign detection rely on handcrafted features and classical machine learning algorithms, which often struggle to achieve high accuracy in complex real-world environments. In contrast, deep learning techniques, particularly Convolutional Neural Networks (CNNs), have shown remarkable performance in both detecting and recognizing traffic signs in diverse conditions. This paper reviews the application of deep learning methods for TSDR, focusing on recent (...) advancements in the use of CNNs, Region-based CNNs (R-CNNs), and YOLO (You Only Look Once) for real-time detection and recognition of traffic signs. We discuss the challenges, including the variability of traffic signs, environmental factors, and computational requirements, and present solutions proposed in the literature. The paper also highlights future directions for improving the robustness, accuracy, and speed of traffic sign detection and recognition systems, with an emphasis on real-time applications. (shrink)