Multi-year track record in video and image analysis applied to real urban security challenges. Crowd dynamics, perimeter monitoring, suspicious behavior flagging, and violence detection, across public and private spaces.
Complex vision challenges, practical engineering
Oxagile builds custom object recognition software: detection, tracking, and behavioral analysis engineered around your data, your cameras, and the environment it deploys into.
Since 2005, we’ve built computer vision systems for clients who can’t afford margin for error. Broadcasters, healthcare providers, retailers, and public safety operators, each with their own data constraints, latency requirements, and compliance obligations.
That work includes a proctoring platform monitoring 5,000+ exams daily and a computer vision video analysis platform for law enforcement, both in the projects below.
Where off-the-shelf fails
The conditions vendors test for aren’t the ones you’ll face in production. The real engineering challenge starts after the proof of concept, when computer vision systems are adapted to the operational realities they weren’t originally built for.
Model drift in production
Generic models trained on benchmark datasets degrade against your specific lighting conditions, camera angles, and object variance. The gap between lab accuracy and production accuracy is wide. Custom training on your data, not a fine-tuned version of someone else’s, is the only reliable fix.
Domain-specific compliance
Healthcare imaging, law enforcement video, and financial biometrics each carry distinct regulatory constraints around data residency, retention, and access control. Generic systems are rarely architected with those constraints in mind, nor do they pass a serious vendor qualification review.
Latency vs. accuracy trade-offs
Real-time detection at scale forces architectural decisions that off-the-shelf platforms leave to you: edge vs. server-side deployment, frame batching, model compression, hardware selection. Get them wrong and you’re choosing between a system that’s fast enough or accurate enough, but not both.
Integration friction
Pre-trained models output in formats and at throughputs that hardly ever match your existing stack. The model itself is often the easy part. Building the serving layer, designing the API contract, and fitting the pipeline into your infrastructure is where most PoCs stall and timelines slip.
Why retrofit when you can build for it?
Object recognition solutions built around your data, environment, and operational constraints, then deployed where the problem lives.
Ready to deploy object recognition beyond the lab?
Object recognition solutions spanning detection, tracking, and behavioral analysis, built for the complexity of real-world environments.
Object tracking
Bringing together computer vision and operational expertise to improve safety in complex real-world environments.
- Identification and tracking of static and dynamic objects
- Real-time multi-object tracking
- Crowd analysis and control
- Social distancing monitoring
- Comprehensive object profile creation
- Walking route analysis
- Handling occlusions and complex object interactions
Object recognition development tech stack
DL frameworks
TensorFlow • PyTorch • Core ML • Caffe2 • Theano • Sonnet • Microsoft Cognitive Toolkit
Modules/Toolkits
Kurento’s computer vision module • NVIDIA DeepStream SDK • TensorRT • GStreamer
Services
Google Cloud AI • Amazon Machine Learning • Azure Machine Learning
Hardware
Server • Desktop • Edge devices • Cloud • Mobile • Tablet
Recognition starts with the right inputs
Bring us the images, video, accuracy targets, or technical constraints, and our engineers will help you figure out what it takes to make it work.
Why choose Oxagile
Real-world applications
SafetySAFE CITIES
PROCTORINGEDTECH PROCTORING
Enterprise-grade proctoring built on biometric analysis. Facial expressions, posture shifts, and gaze patterns are read in real time to assess student engagement and flag irregularities. Every exam session stays audit-ready and regulation-compliant.
AUDIENCE INTELAUDIENCE INTEL
Face and emotion recognition applied to audience analytics. Demographic profiling, attention curves, and reaction mapping give broadcasters and platforms the data they need. Sharper content decisions, more precise ad timing, and recommendations that reflect actual viewer behavior.
MARTECHMARTECH
Object and emotion recognition that reads actual customer responses, letting brands move beyond assumed preferences when shaping messaging and creatives.
Loyalty programs gain a practical edge too: recognized returning customers get a seamless, personalized experience from the moment they walk in.
FAQ
Yes, and it’s one of the more demanding configurations. Real-time performance depends on model architecture, hardware, and how the pipeline is optimized for latency.
Oxagile’s video object detection software is built to handle continuous streams without frame-drop or lag in detection, whether deployed on edge devices or server-side infrastructure. The specific throughput and accuracy trade-offs get scoped during architecture design based on your environment and volume.
Image classification assigns a label to an entire image. Object identification software goes further, locating specific instances within a frame and identifying what they are.
Object detection combines both: it draws bounding boxes around objects and classifies each one, often tracking multiple targets simultaneously. In practice, most production systems use detection as the foundation, with recognition and classification layered on top depending on the use case.
Oxagile builds all three and tunes the detection and classification trade-offs to the use case.
| Term | What it outputs | Where it fits |
|---|---|---|
| Image classification | One label for the whole image | Frame level sorting and tagging |
| Object detection | A bounding box and a class for each object, often several at once | The foundation of most production systems |
| Object recognition | The identity of a specific instance inside the frame | Layered on detection where the system has to know which object, not just that there is one |
Data governance is treated as an architectural concern, not an afterthought. Oxagile develops object detection services under ISO 27001-certified practices, with data handling protocols defined at the project outset, covering storage, access control, anonymization where required, and compliance with relevant regulations.
For sensitive deployments, on-premise processing is an option that keeps data within your own infrastructure entirely.
Two to four weeks for a focused PoC: a single object class, a controlled environment, clear success criteria. It varies by complexity, data availability, and how well-defined the target use case is. Multi-class detection in uncontrolled conditions with custom training data takes longer. Oxagile grounds that timeline in the scoping conversation upfront.
Yes. Integration is part of the delivery, not a separate engagement. Oxagile works with your existing stack, APIs, data pipelines, front-end interfaces, or embedded systems, and designs the model output format and serving layer to fit how your infrastructure actually operates. Where legacy constraints exist, those get identified and addressed during the technical discovery phase.
Accuracy depends on your footage, not on a benchmark score. Oxagile baselines precision and recall on your own frames during the PoC and reports both before a full build is scoped. Vendor accuracy figures come from clean datasets and rarely survive your lighting, camera angles, and object variance.
