In this work, we combine Curriculum Learning with Deep Reinforcement Learning to learn without any prior domain knowledge, an end-to-end competitive driving policy for the CARLA autonomous driving simulator. To our knowledge, we are the first to provide consistent results of our driving policy on all towns available in CARLA. Our approach divides the reinforcement learning phase into multiple stages of increasing difficulty, such that our agent is guided towards learning an increasingly better driving policy. The agent architecture comprises various neural networks that complements the main convolutional backbone, represented by a ShuffleNet V2. Further contributions are given by (i) the proposal of a novel value decomposition scheme for learning the value function in a stable way and (ii) an ad-hoc function for normalizing the growth in size of the gradients. We show both quantitative and qualitative results of the learned driving policy.

Technological evolution has enabled the development of new artificial intelligence (AI) models with generative capabilities. Among them, one of the most discussed is the virtual agent ChatGPT. This chatbot may occasionally produce fake information, as also declared by the producer OpenAI. Such a model may provide very useful support in several tasks, ranging from text summarization to programming. The research community has marginally investigated the impact that fake information created by AI models has on the users’ perceptions and on their belief in AI. We analyzed the impact of the fake information produced by AI on user perceptions, specifically trust and satisfaction, by performing a user study on ChatGPT. An additional issue is assessing whether the early or late knowledge of the possibility of the tool generating fake information has a different impact on the users’ perceptions. We conducted an experiment, involving 62 university students, a category of users who may employ tools such as ChatGPT extensively. The experiment consisted of a guided interaction with ChatGPT. Some of the participants experienced the failure of the chatbot, while a control group only received correct and reliable answers. We collected participants’ perceptions of trust, satisfaction, and usability, together with the net promoter score (NPS). The results demonstrated a statistically significant difference in trust and satisfaction between the users who early experienced fake information production compared to those who discovered ChatGPT’s faulty behaviors later during the interaction. Also, there is no statistically significant difference among the users who received the late fake information and the control group (no fake information). Usability and the NPS also resulted higher when the fake news was detected in the late interaction. When users are aware of the fake information generated by ChatGPT their trust and satisfaction decrease, especially when they impact on this at the early stage of use of the chatbot. Nevertheless, the perception of trust and satisfaction still remains high, as some of the users are still enthusiastic; others consider a more conscious use of the tool in terms of support to be verified. A useful strategy could be to favor a critical use of ChatGPT, letting young people to verify the provided information. This should be a new way to perform learning activities.

Head pose estimation is a sensitive topic in video surveillance/smart ambient scenarios since head rotations can hide/distort discriminative features of the face. Face recognition would often tackle the problem of video frames where subjects appear in poses making it quite impossible. In this respect, the selection of the frames with the best face orientation can allow triggering recognition only on these, therefore decreasing the possibility of errors. This paper proposes a novel approach to head pose estimation for smart cities and video surveillance scenarios, aiming at this goal. The method relies on a cascade of two models: the first one predicts the positions of 68 wellknown face landmarks; the second one applies a web-shaped model over the detected landmarks, to associate each of them to a specific face sector. The method can work on detected faces at a reasonable distance and with a resolution that is supported by several present devices. Results of experiments executed over some classical pose estimation benchmarks, namely Point ’04, Biwi, and AFLW datasets show good performance in terms of both pose estimation and computing time. Further results refer to noisy images that are typical of the addressed settings. Finally, examples demonstrate the selection of the best frames from videos captured in video surveillance conditions

According to the Wall Street Journal, one billion surveillance cameras will be deployed around the world by 2021. This amount of information can be hardly managed by humans. Using a Inflated 3D ConvNet as backbone, this paper introduces a novel automatic violence detection approach that outperforms state-of-the-art existing proposals. Most of those proposals consider a pre-processing step to only focus on some regions of interest in the scene, i.e., those actually containing a human subject. In this regard, this paper also reports the results of an extensive analysis on whether and how the context can affect or not the adopted classifier performance. The experiments show that context-free footage yields substantial deterioration of the classifier performance (2% to 5%) on publicly available datasets. However, they also demonstrate that performance stabilizes in context-free settings, no matter the level of context restriction applied. Finally, a cross-dataset experiment investigates the generalizability of results obtained in a single-collection experiment (same dataset used for training and testing) to cross-collection settings (different datasets used for training and testing).

Head pose estimation methods evaluate the amount of head rotation according to two or three axis, aiming at optimizing the face acquisition process, or extracting neutral-pose frames from a video sequence. Most approaches to pose estimation exploit machine-learning techniques requiring a training phase on a large number of positive and negative examples. In this paper, a novel pose estimation method that exploits a quad-tree based representation of facial features is described. The locations of a set of landmarks detected over the face image guide its subdivision into smaller and smaller quadrants based on the presence or lack of landmarks within each quadrant. The proposed pose descriptor is both effective and efficient, providing accurate yaw, pitch and roll axis estimates almost in real-time, without need for any training or previous knowledge about subject. Experiments conducted on both the BIWI Kinect Head Pose Database and the challenging Automated Facial Landmarks in the Wild dataset, highlight a pose estimate precision exceeding the state of the art with regard to methods not involving training and machine learning approaches.