AI & Computer Vision for Smart Cameras and Embedded Systems

Computer Vision & Sensor Fusion

We develop advanced computer vision algorithms that integrate data from multiple sensors — such as RGB cameras, radars, and PIRs — to improve detection and tracking accuracy. Our expertise includes object detection (YOLO, MobileNet, Cascader, R-CNN, DETR, RT-DETR, Grounding DINO), tracking, segmentation (DeepLab, U-NET, Mask DINO, SAM, YOLO-seg), classification (MobileNets, CovnNeXts, ViT, Swins, and more), and sensor calibration for synchronizing diverse data sources.

Edge AI & Cloud AI

We build AI systems that run efficiently on resource-constrained devices and scale across cloud platforms. Our edge models are optimized using quantization-aware training, model pruning, and acceleration via TensorRT, Ambarella CVflow, and Qualcomm SNPE. In the cloud, we support high-throughput pipelines for model training, updates, and video analytics—all designed for real-time responsiveness and cross-device consistency.

Motion Detection & Behavior Analysis

We build models that detect motion and recognize behavior in real time. Using a mix of signal processing (optical flow, motion history) and deep learning (3D CNNs, LSTMs), we distinguish between ordinary movement and unusual activity—like loitering, pacing, or person-vs-pet detection. These models help automate alerts and improve awareness in security and smart home systems.

AI Models Training & Evaluation

We build AI training pipelines that support repeatable, efficient model development and deployment. Our team works with frameworks like TensorFlow, PyTorch, and ONNX Runtime, integrating dataset versioning, augmentation tools, and automated workflows that test models as they evolve. We evaluate performance in varied conditions—such as lighting shifts, winds, surrounding shadows or device-specific noise—and monitor accuracy across firmware updates to keep results consistent over time.

Data Collection & Preparation

We establish robust data pipelines for collecting, annotating, and managing large-scale datasets essential for training and validating computer vision models. Our expertise includes synthetic data generation, data augmentation strategies, and ensuring data quality and diversity to prevent bias and improve model generalization.

AI for Embedded Systems

We adapt computer vision models to run efficiently on microcontrollers and embedded chips. This includes model pruning, post-training quantization, and deployment through SDKs like Ambarella CVflow and SigmaStar. We also manage memory profiling and inference tuning to meet real-time performance requirements.

AI Algorithms for Security & Surveillance

We build AI-driven security solutions that support event-based detection and on-device decision-making. Our work includes person and vehicle detection, event classification, anomaly detection, and forensic video indexing & search. We design models to reduce false alerts and integrate them with ISP pipelines, edge inferencing stacks, and metadata generation workflows—improving response time and alert quality in real-world security environments.

AI Research & Innovation

We explore new methods in AI to improve efficiency and accuracy for vision systems. Our research includes self-supervised learning techniques (SimCLR, BYOL), compact model architectures (EfficientNet, MobileViT), and generative approaches for synthetic data and video understanding. We focus on bringing these developments into production by validating them on real-world hardware and integrating them into deployment pipelines.

Large Language Models (LLMs)

We explore large language models alongside vision systems to support features like natural-language querying, camera summarization, and contextual AI alerts. We optimize these systems using prompt tuning, low-rank adaptation (LoRA), and multimodal embeddings to meet hardware and latency constraints.

MLOps

We manage data pipelines, container orchestration, CI/CD, and experiment tracking with in-house developed tools. Leveraging AWS, Kubernetes, KubeFlow, and Terraform, we automate and monitor a robust infrastructure platform that ensures high performance, availability, and scalability for diverse workloads—bridging the gap between cutting-edge models and real-world production environments.