BatchParameters Class Reference

Class containing a set of parameters for batch object detection. More...

Functions
BatchParameters	__cinit__ (self, enable=False, id_retention_time=240, batch_duration=2.0)
	Default constructor. More...
bool	enable (self)
	Whether to enable the batch option in the object detection module. More...
float	id_retention_time (self)
	Max retention time in seconds of a detected object. More...
float	latency (self)
	Trajectories will be output in batch with the desired latency in seconds. More...

Detailed Description

Class containing a set of parameters for batch object detection.

The default constructor sets all parameters to their default settings.

Note

Parameters can be adjusted by the user.

Functions

__cinit__()

BatchParameters __cinit__	(		self,
			enable = False,
			id_retention_time = 240,
			batch_duration = 2.0
	)

Default constructor.

All the parameters are set to their default values. param enable : Activates enable param id_retention_time : Chosen id_retention_time param batch_duration : Chosen latency

enable()

bool enable

(

self

)

Whether to enable the batch option in the object detection module.

Batch queueing system provides:

• deep-learning based re-identification
• trajectory smoothing and filtering

Default: False

Note

To activate this option, enable must be set to True.

id_retention_time()

float id_retention_time

(

self

)

Max retention time in seconds of a detected object.

After this time, the same object will mostly have a different id.

latency()

float latency

(

self

)

Trajectories will be output in batch with the desired latency in seconds.

During this waiting time, re-identification of objects is done in the background.

Note

Specifying a short latency will limit the search (falling in timeout) for previously seen object ids but will be closer to real time output.

Specifying a long latency will reduce the change of timeout in re-identification but increase difference with live output.