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Wild Animal Detection

Wild Animal Detection involves finding wildlife or pets in photos or videos. Traditional computer vision techniques, such as motion detection, caused false alarms and drained camera batteries, but deep learning has improved accuracy and added the ability to classify animal species. This is crucial for monitoring specific animals in the wild and alerting people to pests or predators.

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Smart homes and invasive animals

  • The size of AI models aligns with the chipset and accuracy vs performance needs of the application
  • Small models are efficient for low-power devices, while larger models offer higher accuracy and are optimized for powerful GPU's.

Security and False Alarm Reduction

  • False alarms in security and surveillance systems caused by animals are a common issue, especially when relying solely on motion detection.
  • Our animal detector solves this problem by detecting wildlife using deep learning or filtering existing alarms using an animal classifier, reducing false alarms to a minimum and avoiding alarm fatigue for operators.

Species Classification and Tracking

  • Animal detectors identify the presence and location of animals in videos and use another AI model to classify the detection into specific species or types.
  • This enables tracking, recording, and census of specific animals while saving battery life and reducing data transfers.
  • Our AI models, trained using synthetic data, can classify any animal, even with limited real-world training data, making it suitable for rare animals.

Optimization of Object Detection.

  • Finding smaller individuals becomes increasingly challenging for a people detector, but it also enables the use of lower resolution cameras or cheaper lenses, making it a balancing act.
  • This project helps you find the optimal strategy for your application by allowing you to experiment with different settings and models.

Attendify

Face recognition attendance systems utilize artificial intelligence (AI) to accurately identify individuals based on facial features. These systems offer a modern and efficient approach to attendance tracking, replacing traditional methods such as manual entry or swipe cards.

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Precision Through Facial Recognition

  • Face recognition technology eliminates human errors inherent in manual attendance tracking methods, ensuring precise and reliable recording of employee hours.
  • By accurately identifying individuals based on facial features, the system minimizes the risk of attendance discrepancies and improves overall data integrity.

Biometric Identity Verification

  • Leveraging biometric authentication, face recognition systems enhance security measures by verifying individuals' identities with a high level of certainty.
  • This reduces the likelihood of unauthorized access or fraudulent attendance practices, bolstering organizational security protocols and protecting sensitive data.

Automated Attendance Management

  • Automated face recognition attendance systems streamline the attendance management process, significantly reducing the time and effort required for administrative tasks.
  • With swift and accurate identification of individuals, administrators can allocate their time more efficiently to other important responsibilities, increasing overall productivity.

Regulatory Compliance Assurance

  • Face recognition attendance systems help organizations adhere to labor regulations and audit requirements by maintaining meticulous and accurate attendance records.
  • By ensuring compliance with legal standards, the system mitigates the risk of penalties or legal repercussions, fostering a culture of transparency and accountability within the organization.