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Researchers built a system that classifies vehicles using colour‑component template matching, running at four frames per second. Withdrawn September 2025. Read more: https://getnews.me/real-time-vehicle-classification-using-colour-based-template-matching/ #vehicledetection #templatematching
Real-Time Vehicle Classification Using Colour-Based Template Matching
September 25, 2025 at 11:07 AM
azoai.bsky.social
1/3.🚦📹🤖 Enhancing traffic surveillance with Faster R-CNN for vehicle segmentation revolutionizes urban mobility. www.azoai.com/news/2024051... #AI #Traffic #Technology #Innovation #SmartCities #Surveillance #VehicleDetection #DataAnalysis #UrbanMobility #Safety @natureportfolio.bsky.social
Smart Traffic Surveillance: Faster R-CNN for Vehicle Segmentation
Researchers introduced a novel deep learning approach based on faster R-CNN for segmenting vehicles in traffic videos, addressing challenges like occlusions and varying traffic densities. Through adap...
www.azoai.com
May 15, 2024 at 4:50 AM
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Detecting earthquakes or just passing cars? 🌍🚗 A research uses deep learning to distinguish seismic signals from earthquakes, vehicles, and noise with 99% accuracy!📡🔬

#MachineLearning #TrafficMonitoring #VehicleDetection #EarthquakeDetection
https://link.springer.com/article/10.1007/s10950-024-10267-8 Figure 2: Frequency Analysis of Noise, Vehicle, and Earthquake Signals Axes Explanation: X-Axis (Frequency in Hz): Represents the frequency content of the seismic signals. Frequency (Hertz, Hz) refers to the number of oscillations per second. Y-Axis (Amplitude, not explicitly labeled but inferred): Represents the strength or power of the seismic signal at different frequencies. Color Representation: Green: Noise signal components. Blue: Vehicle-related seismic signals. Red: Earthquake-related seismic signals. Types of Lines (Dotted vs. Solid): Each color has three dotted lines, corresponding to the East, North, and Vertical components of motion. - East, North, and Vertical refer to the three axes of ground motion measured by a seismometer. - Vertical component measures up-down movement. - East and North components measure horizontal movements in their respective directions. Interpretation & Relevance: Noise (Green): Dominates at high frequencies (>30 Hz), which indicates environmental or anthropogenic disturbances. Vehicle (Blue): Peaks between 5 Hz and 20 Hz, showing the frequency range of seismic signals generated by passing vehicles. Earthquake (Red): Stronger in the lower frequency range (0-10 Hz), which is typical for seismic events as earthquake waves have lower frequencies and longer wavelengths. Relevance to the Study: Helps in distinguishing between different seismic sources by analyzing their frequency characteristics. Relevance to Real Life: Understanding frequency differences is crucial for earthquake detection, noise filtering, and distinguishing between natural and human-made seismic events. Figure 6: Model Prediction for Noise, Earthquake, and Vehicle Seismic Signals Over Time Axes Explanation: X-Axis (Time in Minutes, 0–60 min): Represents an hour-long seismic recording. Y-Axis (Seismic Signal & Model Prediction): Represents seismic activity in three orientations: - East-West (Top Panel) - North-South (Middle Panel) - Vertical (Bottom Panel) Color Representation: Red (Earthquake): Indicates moments when the model predicts earthquake activity. Blue (Vehicle): Shows periods when the model detects seismic signals from vehicles. Green (Noise): Represents background noise detected by the model. Black (Seismic Data): The actual recorded seismic signal. Interpretation & Relevance: The model successfully identifies different sources of seismic activity. Noise (Green) is persistent and appears frequently in the vertical component. Vehicles (Blue) appear intermittently in the North-South component, matching expected vehicle movement. Earthquakes (Red) are rare and appear as spikes in the East-West component. Relevance to the Study: Demonstrates the model's capability to classify seismic events accurately. Relevance to Real Life: Helps in automatic seismic monitoring, reducing false alarms, and improving earthquake early warning systems. Figure 9: Impact of Time-Shifted Vehicle Noise on Earthquake Seismic Signals and Their Frequency Response X and Y Axes Left Side (Time-Domain Signal) - X-axis: Time (in seconds) → Shows how the signal changes over time. - Y-axis: Amplitude (measured in acceleration or velocity) → Represents the strength of the signal. A strong earthquake will have a high-amplitude waveform, while weaker signals will have lower amplitudes. Right Side (Frequency-Domain - FFT Representation) - X-axis: Frequency (Hz) → Represents how often a certain vibration occurs per second. - Y-axis: Amplitude (Intensity in frequency domain) → Shows how strong the signal is at different frequencies. FFT helps separate different sources of vibrations: - Low frequencies (~0-5 Hz): Typically associated with earthquakes. - Higher frequencies (~10-20 Hz): More likely caused by vehicles or human activity. Colors Red Line: Represents the original earthquake signal. Blue Line: Represents the vehicle signal (vibrations caused by a vehicle). Black Line: Represents the combined signal (earthquake + vehicle noise). By comparing these colors, researchers can determine how vehicle noise affects earthquake signal detection. Type of Lines Used Solid lines are used for all signals, but different colors distinguish earthquake signals, vehicle-induced noise, and their combination. The presence of overlapping or diverging lines indicates interference between signals. Explanation of Technical Words & Abbreviations FFT (Fast Fourier Transform): A mathematical technique that converts a time-domain signal into a frequency-domain signal. It helps analyze the different sources contributing to the signal. Why This Figure is Important (Scientific & Practical Relevance) For Earthquake Studies: Helps understand how human activity (e.g., vehicle movement) interferes with seismic readings. This can improve earthquake detection and help seismologists filter out noise. For Urban Planning. For Public Safety.
February 25, 2025 at 2:41 AM
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September 13, 2025 at 6:53 PM