INSEM - Camea SV

1. Stage of exploring the possibilities of merger and preparation of the initial
set of procedures

1. 7. 2022 30.11.2022 Exploring the possibility of
processing the fusion of camera data (image) and sensor data obtained from a 3D
radar sensor (point cloud) and processing procedures for such data fusion. The
aim is in particular:

Prepare, in cooperation with CAMEA (and another
cooperating company COGNITECHNA), a dataset of traffic scenes with image and
radar data as well as with annotations (at least 500 shots). Prepare the
structure of the neural network and prepare the input data so that it is
compatible with the network for the purpose of data analysis for object
detection, classification and categorization. Prepare algorithms implementing
data analysis by neural network (CNN type), select a suitable library for this
software to run in the smart camera system CAMEA (based on Intel Atom).
Experimentally implement the initial version of the network configuration and
verify compatibility and basic functionality (for example, on vehicle detection).


2. Stage of merger optimization and processing of a verified set of
procedures

1. 12. 2022 31.5.2023 Optimization of fusion parameters and
delivery of an updated version of the relevant set of procedures for image and 3D
data fusion (point cloud). The aim is in particular:

Optimize the
detection procedure itself so that it detects objects in traffic with
a reliability of at least 90% (exact metrics and categories of objects will be
specified during the 1st stage). Accelerate detection so that real-time detection
takes place on the smart camera system (faster than the flow of objects, the
exact parameters will be specified in the 1st stage). Prepare procedures
implementing data analysis by neural network and prepare it for operation both on
the collected dataset and in the conditions of real data entry. Verify the
prepared optimized software in laboratory conditions close to real traffic (from
the point of view of algorithms and data flow, not necessarily in deployment
directly in road traffic).