How Do You Handle ‘Temporal Consistency’ on the Edge to Prevent Flickering Detections From Triggering False Actions?

Object detectors such as YOLO and EfficientDet treat each video frame independently. This works fine for static images, but in real-time video streams, it causes detections to flicker. Bounding boxes jitter, confidence scores oscillate near thresholds, and objects "blink" in and out of existence.

In a display overlay, this is merely annoying. In a closed-loop control system where detections trigger actuators, it can be catastrophic. A flickering detection might cause a robotic gripper to spasm, a security gate to oscillate, or a safety system to flood operators with false alarms.

The solution is building a temporal consistency layer between your detector and your actuation logic.

What Causes Detections to Flicker in the First Place?

Three distinct types of instability compound into the flickering you observe:

1. Position jitter occurs when the bounding box coordinates fluctuate rapidly, even for stationary objects. This stems from regression uncertainty in the detection head, sensor noise, and the discrete nature of anchor box scales.

2. Confidence fluctuation happens when the class probability oscillates around your threshold. If your threshold is 0.5 and the detector outputs [0.51, 0.49, 0.52, 0.48] across four frames, you get a binary presence signal that toggles every frame.

3. Existence flicker is the complete loss and re-acquisition of detections due to occlusion, motion blur, or extreme poses. This is especially problematic on edge devices running quantized INT8 models, where the reduced precision amplifies decision boundary noise.

How Do You Stabilize Bounding Box Coordinates?

For smoothing spatial coordinates, the One Euro Filter outperforms traditional approaches on edge devices. Standard filters force you to choose between low jitter (high smoothing, high latency) and fast response (low smoothing, noisy output). The One Euro Filter sidesteps this trade-off by adapting its cutoff frequency based on signal velocity.

When the tracked object is stationary, the filter applies heavy smoothing to eliminate micro-jitter. When the object moves quickly, the filter opens up to track motion with minimal lag. This adaptive behavior is ideal for robotics and human-computer interaction, where both stability and responsiveness matter.

The filter is also computationally trivial, requiring only scalar arithmetic with no matrix operations. It handles variable frame rates gracefully, which is critical on thermally-throttled edge hardware where inference times fluctuate.

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
import  math
import  time

class  LowPassFilter:
    def  __init__(self):
        self.previous  =  None
    def  __call__(self,  x:  float,  alpha:  float)  ->  float:
        if  self.previous  is  None:
            self.previous  =  x
        else:
            self.previous  =  alpha  *  x  +  (1  -  alpha)  *  self.previous
        return  self.previous

class  OneEuroFilter:
    def  __init__(self,  min_cutoff:  float  =  1.0,  beta:  float  =  0.007,  d_cutoff:  float  =  1.0):
        """
        min_cutoff:  minimum  cutoff  frequency  (Hz).  Lower  =  more  smoothing  when  stationary.
        beta:  speed  coefficient.  Higher  =  less  lag  during  fast  motion.
        d_cutoff:  cutoff  frequency  for  derivative  estimation.
        """
        self.min_cutoff  =  min_cutoff
        self.beta  =  beta
        self.d_cutoff  =  d_cutoff
        self.x_filter  =  LowPassFilter()
        self.dx_filter  =  LowPassFilter()
        self.last_time  =  None

    def  _smoothing_factor(self,  t_e:  float,  cutoff:  float)  ->  float:
        tau  =  1.0  /  (2  *  math.pi  *  cutoff)
        return  1.0  /  (1.0  +  tau  /  t_e)

    def  __call__(self,  x:  float,  t:  float  =  None)  ->  float:
        if  t  is  None:
            t  =  time.time()

        if  self.last_time  is  None:
            self.last_time  =  t
            return  self.x_filter(x,  1.0) #  no  smoothing  on  first  sample

        t_e  =  t  -  self.last_time
        if  t_e  <=  0:
            t_e  =  1e-6
        self.last_time  =  t

        #  estimate  derivative
        dx  =  (x  -  (self.x_filter.previous  or  x))  /  t_e
        alpha_d  =  self._smoothing_factor(t_e,  self.d_cutoff)
        dx_smooth  =  self.dx_filter(dx,  alpha_d)

        #  adapt  cutoff  based  on  speed
        cutoff  =  self.min_cutoff  +  self.beta  *  abs(dx_smooth)
        alpha  =  self._smoothing_factor(t_e,  cutoff)
        return  self.x_filter(x,  alpha)

class  BoundingBoxSmoother:
    def  __init__(self,  min_cutoff:  float  =  1.0,  beta:  float  =  0.007):
        """Applies  One  Euro  filtering  independently  to  each  bounding  box  coordinate."""
        self.filters  =  {
            'x1':  OneEuroFilter(min_cutoff,  beta),
            'y1':  OneEuroFilter(min_cutoff,  beta),
            'x2':  OneEuroFilter(min_cutoff,  beta),
            'y2':  OneEuroFilter(min_cutoff,  beta),
        }

    def  __call__(self,  bbox:  tuple,  t:  float  =  None)  ->  tuple:
        """
        bbox:  (x1,  y1,  x2,  y2)  raw  detection  coordinates
        returns:  smoothed  (x1,  y1,  x2,  y2)
        """
        x1,  y1,  x2,  y2  =  bbox
        return  (
            self.filters['x1'](x1,  t),
            self.filters['y1'](y1,  t),
            self.filters['x2'](x2,  t),
            self.filters['y2'](y2,  t),
        )

To use this in a detection loop:

1
2
3
4
5
6
smoother  =  BoundingBoxSmoother(min_cutoff=0.5,  beta=0.01)

for  frame  in  video_stream:
    detections  =  detector(frame)
    for  det  in  detections:
        det.bbox  =  smoother(det.bbox)

Tune min_cutoff lower for more aggressive smoothing when stationary. Increase beta if the smoothed box lags during fast motion.

For simpler scenarios with predictable linear motion (e.g., conveyor belts, vehicle tracking), Double Exponential Smoothing yields good results. It explicitly models velocity, compensating for the lag inherent in basic exponential smoothing.

How Do You Prevent the Detection State From Toggling?

Smoothing coordinates does not solve the binary present/absent flickering. For that, you need logical debouncing.

• Hysteresis thresholding introduces two thresholds instead of one. Set an upper threshold (e.g., 0.6) for activation and a lower threshold (e.g., 0.4) for deactivation. An object must exceed 0.6 confidence to trigger the system, but once triggered, the system holds even if confidence dips to 0.5. This prevents rapid toggling when confidence hovers near a single threshold.

• N-out-of-M confirmation requires an object to be detected in M of the last N frames before it is considered valid. A setting of 3-out-of-5 filters transient false positives (ghosts appearing for a single frame) while remaining responsive enough for real-time applications.

• Time-to-Live (TTL) prevents premature track termination. When a detection disappears, maintain its existence for a grace period (e.g., 500ms). If it reappears within that window, preserve the identity and suppress the disappearance event entirely. This is essential for people counting, where someone walking behind a pillar should not be registered as two separate individuals.

Which Tracker Should You Use on Resource-Constrained Hardware?

ByteTrack has emerged as the optimal choice for edge deployments. Its key innovation is the intelligent handling of low-confidence detections.

Standard trackers discard detections below a confidence threshold (say, 0.5) before association. If an object becomes partially occluded and its confidence drops to 0.4, the track dies and restarts later with a new ID. ByteTrack instead retains all detections and divides them into high- and low-confidence groups. It first matches high-confidence detections to existing tracks, then matches remaining unmatched tracks to low-confidence detections.

This "rescue" mechanism dramatically reduces ID switches during occlusions. Because ByteTrack relies solely on motion and geometry (without a heavy Re-ID neural network), it runs significantly faster than DeepSORT while delivering superior tracking accuracy.

What Does a Complete Edge Pipeline Look Like?

The layers work together. ByteTrack maintains object identity through temporary detection failures. The One Euro Filter smooths each track's coordinates without introducing lag during motion. N-out-of-M and TTL logic ensure transient glitches never reach your actuation layer. Hysteresis prevents the final binary signal from chattering.

Temporal consistency requires treating the entire pipeline as a system, not bolting filters onto a frame-by-frame detector as an afterthought.