DC-SPP-YOLO: Dense Connection and Spatial Pyramid Pooling Based YOLO for Object Detection

Although YOLOv2 approach is extremely fast on object detection; its backbone network has the low ability on feature extraction and fails to make full use of multi-scale local region features, which restricts the improvement of object detection accuracy. Therefore, this paper proposed a DC-SPP-YOLO (Dense Connection and Spatial Pyramid Pooling Based YOLO) approach for ameliorating the object detection accuracy of YOLOv2. Specifically, the dense connection of convolution layers is employed in the backbone network of YOLOv2 to strengthen the feature extraction and alleviate the vanishing-gradient problem. Moreover, an improved spatial pyramid pooling is introduced to pool and concatenate the multi-scale local region features, so that the network can learn the object features more comprehensively. The DC-SPP-YOLO model is established and trained based on a new loss function composed of mean square error and cross entropy, and the object detection is realized. Experiments demonstrate that the mAP (mean Average Precision) of DC-SPP-YOLO proposed on PASCAL VOC datasets and UA-DETRAC datasets is higher than that of YOLOv2; the object detection accuracy of DC-SPP-YOLO is superior to YOLOv2 by strengthening feature extraction and using the multi-scale local region features.Comment: 23 pages, 9 figures, 9 table

Are You in the Line? RSSI-based Queue Detection in Crowds

Crowd behaviour analytics focuses on behavioural characteristics of groups of people instead of individuals' activities. This work considers human queuing behaviour which is a specific crowd behavior of groups. We design a plug-and-play system solution to the queue detection problem based on Wi-Fi/Bluetooth Low Energy (BLE) received signal strength indicators (RSSIs) captured by multiple signal sniffers. The goal of this work is to determine if a device is in the queue based on only RSSIs. The key idea is to extract features not only from individual device's data but also mobility similarity between data from multiple devices and mobility correlation observed by multiple sniffers. Thus, we propose single-device feature extraction, cross-device feature extraction, and cross-sniffer feature extraction for model training and classification. We systematically conduct experiments with simulated queue movements to study the detection accuracy. Finally, we compare our signal-based approach against camera-based face detection approach in a real-world social event with a real human queue. The experimental results indicate that our approach can reach minimum accuracy of 77% and it significantly outperforms the camera-based face detection because people block each other's visibility whereas wireless signals can be detected without blocking.Comment: This work has been partially funded by the European Union's Horizon 2020 research and innovation programme within the project "Worldwide Interoperability for SEmantics IoT" under grant agreement Number 72315

Feature Learning from Spectrograms for Assessment of Personality Traits

Several methods have recently been proposed to analyze speech and automatically infer the personality of the speaker. These methods often rely on prosodic and other hand crafted speech processing features extracted with off-the-shelf toolboxes. To achieve high accuracy, numerous features are typically extracted using complex and highly parameterized algorithms. In this paper, a new method based on feature learning and spectrogram analysis is proposed to simplify the feature extraction process while maintaining a high level of accuracy. The proposed method learns a dictionary of discriminant features from patches extracted in the spectrogram representations of training speech segments. Each speech segment is then encoded using the dictionary, and the resulting feature set is used to perform classification of personality traits. Experiments indicate that the proposed method achieves state-of-the-art results with a significant reduction in complexity when compared to the most recent reference methods. The number of features, and difficulties linked to the feature extraction process are greatly reduced as only one type of descriptors is used, for which the 6 parameters can be tuned automatically. In contrast, the simplest reference method uses 4 types of descriptors to which 6 functionals are applied, resulting in over 20 parameters to be tuned.Comment: 12 pages, 3 figure

Features Extraction on IoT Intrusion Detection System Using Principal Components Analysis (PCA)

There are several ways to increase detection accuracy result on the intrusion detection systems (IDS), one way is feature extraction. The existing original features are filtered and then converted into features with lower dimension. This paper uses the Principal Components Analysis (PCA) for features extraction on intrusion detection system with the aim to improve the accuracy and precision of the detection. The impact of features extraction to attack detection was examined. Experiments on a network traffic dataset created from an Internet of Thing (IoT) testbed network topology were conducted and the results show that the accuracy of the detection reaches 100 percent

Feature extraction from electroencephalograms for Bayesian assessment of newborn brain maturity

We explored the feature extraction techniques for Bayesian assessment of EEG maturity of newborns in the context that the continuity of EEG is the most important feature for assessment of the brain development. The continuity is associated with EEG “stationarity” which we propose to evaluate with adaptive segmentation of EEG into pseudo-stationary intervals. The histograms of these intervals are then used as new features for the assessment of EEG maturity. In our experiments, we used Bayesian model averaging over decision trees to differentiate two age groups, each included 110 EEG recordings. The use of the proposed EEG features has shown, on average, a 6% increase in the accuracy of age differentiation

An Efficient Feature Extraction Scheme for Mobile Anti-Shake in Augmented Reality

In recent years, augmented reality on mobile devices has become popular. Mobile shakes are the most typical type of interference in mobile augmented reality. To negate such interference, anti-shake is an urgent requirement. To enhance anti-shake efficiency, we propose an efficient feature extraction scheme for mobile anti-shake in augmented reality. The scheme directly detects corners to avoid the non-extreme constraint such that the efficiency of feature extraction is improved. Meanwhile, the scheme only updates the added corners during mobile shakes, which improves the accuracy of feature extraction. In the experiments, the memory consumption of existing methods is almost double compared to that in our scheme. Further, the runtime of our scheme is only half of the runtime of the existing methods. The experimental results demonstrate that our scheme performs better than the existing classic methods on mobile anti-shake in terms of memory consumption, efficiency, and accuracy

Improving acoustic vehicle classification by information fusion

We present an information fusion approach for ground vehicle classification based on the emitted acoustic signal. Many acoustic factors can contribute to the classification accuracy of working ground vehicles. Classification relying on a single feature set may lose some useful information if its underlying sound production model is not comprehensive. To improve classification accuracy, we consider an information fusion diagram, in which various aspects of an acoustic signature are taken into account and emphasized separately by two different feature extraction methods. The first set of features aims to represent internal sound production, and a number of harmonic components are extracted to characterize the factors related to the vehicle’s resonance. The second set of features is extracted based on a computationally effective discriminatory analysis, and a group of key frequency components are selected by mutual information, accounting for the sound production from the vehicle’s exterior parts. In correspondence with this structure, we further put forward a modifiedBayesian fusion algorithm, which takes advantage of matching each specific feature set with its favored classifier. To assess the proposed approach, experiments are carried out based on a data set containing acoustic signals from different types of vehicles. Results indicate that the fusion approach can effectively increase classification accuracy compared to that achieved using each individual features set alone. The Bayesian-based decision level fusion is found fusion is found to be improved than a feature level fusion approac