Humans have long fantasized about self-driving vehicles for the sake of luxury, style, safety, and ease. Free road space detection for collision avoidance and path planning is a vital part of autonomous driving vehicles. Despite many researchers focusing on free road space detection, it remains an open and challenging problem for real-world applications. Many studies have attempted to fuse depth and LiDAR features with visual features to improve the overall performance of free road space detection. However, there is no guideline on how such features should be fused to complement the visual features. Additionally, most of the previously proposed methods are computationally expensive and not suitable for real-life applications. The main motivation of this study is to realize a lightweight model that addresses these problems without compromising performance. As the LiDAR and visual features exist in different spaces, the proposed method attempts to learn various transformation and fusion operations from LiDAR features to complement the visual features. To validate the performance of the proposed method, we conduct comprehensive experiments on prominent benchmark datasets. The results of the experiments reveal the superior performance of the proposed model while being lightweight. LRDNet ranks third overall (with a minor difference) and second among LiDAR-based methods on the KITTI road benchmark dataset. Furthermore, the proposed model is the least computationally expensive among state-of-the-art methods and can be considered an optimal trade-off between speed and accuracy.

Few-shot learning is a crucial aspect of modern machine learning that enables models to recognize and classify objects efficiently with limited training data. The shortage of labeled 3D point cloud data calls for innovative solutions, particularly when novel classes emerge more frequently. In this paper, we propose a novel few-shot learning method for recognizing 3D point clouds. More specifically, this paper addresses the challenges of applying few-shot learning to 3D point cloud data, which poses unique difficulties due to the unordered and irregular nature of these data. We propose two new modules for few-shot based 3D point cloud classification, i.e., the Soft Interaction Module (SIM) and Self-Attention Residual Feedforward (SARF) Module. These modules balance and enhance the feature representation by enabling more relevant feature interactions and capturing long-range dependencies between query and support features. To validate the effectiveness of the proposed method, extensive experiments are conducted on benchmark datasets, including ModelNet40, ShapeNetCore, and ScanObjectNN. Our approach demonstrates superior performance in handling abrupt feature changes occurring during the meta-learning process. The results of the experiments indicate the superiority of our proposed method by demonstrating its robust generalization ability and better classification performance for 3D point cloud data with limited training samples.

The field of broadcast sports video analysis requires attention from the research community. Identifying the semantic actions within a broadcast sports video aids better video analysis and highlight generation. One of the key challenges posed to sports video analysis is the availability of relevant datasets. In this paper, we introduce a new dataset SP-2 related to broadcast sports video (available at https://github.com/abdkhanstd/Sports2). SP-2 is a large dataset with several annotations such as sports category (class), playfield scenario, and game action. Along with the introduction of this dataset, we focus on accurately classifying the broadcast sports video category and propose a simple yet elegant method for the classification of broadcast sports video. Broadcast sports video classification plays an important role in sports video analysis as different sports games follow a different set of rules and situations. Our method exploits and explores the true potential of capsule network with dynamic routing, which was introduced recently. First, we extract features using a residual convolutional neural network and build temporal feature sequences. Further, a cascaded capsule network is trained using the extracted feature sequence. Residual inception cascaded capsule network (RICAPS) significantly improves the performance of broadcast sports video classification as deeper features are captured by the cascaded capsule network. We conduct extensive experiments on SP-2 dataset and compare the results with previously proposed methods, and the results show that RICAPS outperforms the previously proposed methods.