Smoke detection is a key component of disaster and accident detection. Despite the wide variety of smoke detection methods and sensors that have been proposed, none has been able to maintain a high frame rate while improving detection performance. In this paper, a smoke detection method for surveillance cameras is presented that relies on shape features of smoke regions as well as color information. The method takes advantage of the use of a stationary camera by using a background subtraction method to detect changes in the scene. The color of the smoke is used to assess the probability that pixels in the scene belong to a smoke region. Due to the variable density of the smoke, not all pixels of the actual smoke area appear in the foreground mask. These separate pixels are united by morphological operations and connected-component labeling methods. The existence of a smoke region is confirmed by analyzing the roughness of its boundary. The final step of the algorithm is to check the density of edge pixels within a region. Comparison of objects in the current and previous frames is conducted to distinguish fluid smoke regions from rigid moving objects. Some parts of the algorithm were boosted by means of parallel processing using CUDA GPUs, thereby enabling fast processing of both low-resolution and high-definition videos. The algorithm was tested on multiple video sequences and demonstrated appropriate processing times for a realistic range of frame sizes.

Currently, many kinds of LiDAR-camera-based 3D object detectors have been developed with two heavy neural networks to extract view-specific features, while a LiDAR-camera-based 3D detector with only one neural network has not been implemented. To tackle this issue, this paper first presents an early-fusion method to exploit both LiDAR and camera data for fast 3D object detection with only one backbone, achieving a good balance between accuracy and efficiency. We propose a novel point feature fusion module to directly extract point-wise features from raw RGB images and fuse them with their corresponding point cloud with no backbone. In this paradigm, the backbone that extracts RGB image features is abandoned to reduce the large computation cost. Our method first voxelizes a point cloud into a 3D voxel grid and utilizes two strategies to reduce information loss during voxelization. The first strategy is to use a small voxel size (0.05m, 0.05m, 0.1m) in X-axis, Y-axis, and Z-axis, respectively, while the second one is to project the feature (e.g. intensity or height information) of point clouds onto RGB images. Numerous experiments evaluated on the KITTI benchmark suite show that the proposed approach outperforms state-of-the-art LiDAR-camera-based methods on the three classes in 3D performance (Easy, Moderate, Hard): cars (88.04%, 77.60%, 76.23%), pedestrians (66.65%, 60.49%, 54.51%), and cyclists (75.87%, 60.07%, 54.51%). Additionally, the proposed model runs at 17.8 frames per second (FPS), which is almost 2× faster than state-of-the-art fusion methods for LiDAR and camera.

As part of the early warning system, forest fire detection has a critical role in detecting fire in a forest area to prevent damage to forest ecosystems. In this case, the speed of the detection process is the most critical factor to support a fast response by the authorities. Thus, this article proposes a new framework for fire detection based on combining color-motion-shape features with machine learning technology. The characteristics of the fire are not only red but also from their irregular shape and movement that tends to be constant at specific locations. These characteristics are represented by color probabilities in the segmentation stage, color histograms in the classification stage, and image moments in the verification stage. A frame-based evaluation and an intersection over union (IoU) ratio was applied to evaluate the proposed framework. Frame-based evaluation measures the performance in detecting fires. In contrast, the IoU ratio measures the performance in localizing the fires. The experiment found that the proposed framework produced 89.97% and 10.03% in the true-positive rate and the false-negative rate, respectively, using the VisiFire dataset. Meanwhile, the proposed method can obtain an average of 21.70 FPS in processing time. These results proved that the proposed method is fast in the detection process and can maintain performance accuracy. Thus, the proposed method is suitable and reliable for integrating into the early warning system.

Facial expression detection is a method to predict human facial emotions. This work is a trending research topic that can be implemented for human-robot interaction. More recently, deep convolutional neural network provides a robust extractor features but tends to be slow in real-time implementations and often requires a large memory and graphics processing units for fast execution. In this article, an efficient CPU-based facial expression detector is proposed using a sequential attention network to improve the baseline performance. The proposed attention network consists of three modules, global representation to capture the global features, channel representation, and dimension representation, which are focused on the channel and using spatial attention to discriminate local features. The efficient partial transfer module is also presented as a light backbone to extract facial features from an image. The entire module is trained and tested on several benchmarks to classify seven facial expressions. As a result, the proposed model reaches an accuracy of 98.18%, 98.75%, 95.63%, and 74.17% on CK+, JAFFE, KDEF, and FER-2013, respectively. It achieves competitive performance when compared to state-of-the-art methods. Lastly, it is integrated with a face detector and runs in real-time without a constraint at 69 frames per second on a CPU.

Vehicle License Plate Detection Method Based on Sliding Concentric Windows and Histogram